diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index c7db58fe8c6a3..c381496164630 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -545,8 +545,25 @@ def sanitize_array(
         data = construct_1d_arraylike_from_scalar(data, len(index), dtype)
         return data
 
+    elif isinstance(data, ABCExtensionArray):
+        # it is already ensured above this is not a PandasArray
+        # Until GH#49309 is fixed this check needs to come before the
+        #  ExtensionDtype check
+        if dtype is not None:
+            subarr = data.astype(dtype, copy=copy)
+        elif copy:
+            subarr = data.copy()
+        else:
+            subarr = data
+
+    elif isinstance(dtype, ExtensionDtype):
+        # create an extension array from its dtype
+        _sanitize_non_ordered(data)
+        cls = dtype.construct_array_type()
+        subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
+
     # GH#846
-    if isinstance(data, np.ndarray):
+    elif isinstance(data, np.ndarray):
         if isinstance(data, np.matrix):
             data = data.A
 
@@ -556,7 +573,10 @@ def sanitize_array(
                 # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
                 # casting aligning with IntCastingNaNError below
                 with np.errstate(invalid="ignore"):
-                    subarr = _try_cast(data, dtype, copy)
+                    # GH#15832: Check if we are requesting a numeric dtype and
+                    # that we can convert the data to the requested dtype.
+                    subarr = maybe_cast_to_integer_array(data, dtype)
+
             except IntCastingNaNError:
                 warnings.warn(
                     "In a future version, passing float-dtype values containing NaN "
@@ -582,28 +602,27 @@ def sanitize_array(
             # we will try to copy by-definition here
             subarr = _try_cast(data, dtype, copy)
 
-    elif isinstance(data, ABCExtensionArray):
-        # it is already ensured above this is not a PandasArray
-        subarr = data
-
-        if dtype is not None:
-            subarr = subarr.astype(dtype, copy=copy)
-        elif copy:
-            subarr = subarr.copy()
+    elif hasattr(data, "__array__"):
+        # e.g. dask array GH#38645
+        data = np.array(data, copy=copy)
+        return sanitize_array(
+            data,
+            index=index,
+            dtype=dtype,
+            copy=False,
+            allow_2d=allow_2d,
+        )
 
     else:
-        if isinstance(data, (set, frozenset)):
-            # Raise only for unordered sets, e.g., not for dict_keys
-            raise TypeError(f"'{type(data).__name__}' type is unordered")
-
+        _sanitize_non_ordered(data)
         # materialize e.g. generators, convert e.g. tuples, abc.ValueView
-        if hasattr(data, "__array__"):
-            # e.g. dask array GH#38645
-            data = np.array(data, copy=copy)
-        else:
-            data = list(data)
+        data = list(data)
 
-        if dtype is not None or len(data) == 0:
+        if len(data) == 0 and dtype is None:
+            # We default to float64, matching numpy
+            subarr = np.array([], dtype=np.float64)
+
+        elif dtype is not None:
             try:
                 subarr = _try_cast(data, dtype, copy)
             except ValueError:
@@ -658,6 +677,14 @@ def range_to_ndarray(rng: range) -> np.ndarray:
     return arr
 
 
+def _sanitize_non_ordered(data) -> None:
+    """
+    Raise only for unordered sets, e.g., not for dict_keys
+    """
+    if isinstance(data, (set, frozenset)):
+        raise TypeError(f"'{type(data).__name__}' type is unordered")
+
+
 def _sanitize_ndim(
     result: ArrayLike,
     data,
@@ -728,7 +755,7 @@ def _maybe_repeat(arr: ArrayLike, index: Index | None) -> ArrayLike:
 
 def _try_cast(
     arr: list | np.ndarray,
-    dtype: DtypeObj | None,
+    dtype: np.dtype | None,
     copy: bool,
 ) -> ArrayLike:
     """
@@ -738,7 +765,7 @@ def _try_cast(
     ----------
     arr : ndarray or list
         Excludes: ExtensionArray, Series, Index.
-    dtype : np.dtype, ExtensionDtype or None
+    dtype : np.dtype or None
     copy : bool
         If False, don't copy the data if not needed.
 
@@ -771,12 +798,6 @@ def _try_cast(
                 return varr
             return maybe_infer_to_datetimelike(varr)
 
-    elif isinstance(dtype, ExtensionDtype):
-        # create an extension array from its dtype
-        array_type = dtype.construct_array_type()._from_sequence
-        subarr = array_type(arr, dtype=dtype, copy=copy)
-        return subarr
-
     elif is_object_dtype(dtype):
         if not is_ndarray:
             subarr = construct_1d_object_array_from_listlike(arr)
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 8f3c93259f0c6..ee7026663b2b6 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -22,6 +22,7 @@
     ArrayLike,
     DtypeObj,
     Manager,
+    npt,
 )
 from pandas.util._exceptions import find_stack_level
 
@@ -1032,7 +1033,7 @@ def _validate_or_indexify_columns(
 
 
 def _convert_object_array(
-    content: list[np.ndarray], dtype: DtypeObj | None
+    content: list[npt.NDArray[np.object_]], dtype: DtypeObj | None
 ) -> list[ArrayLike]:
     """
     Internal function to convert object array.
@@ -1059,6 +1060,7 @@ def convert(arr):
                 arr = cls._from_sequence(arr, dtype=dtype, copy=False)
             else:
                 arr = maybe_cast_to_datetime(arr, dtype)
+
         return arr
 
     arrays = [convert(arr) for arr in content]