diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 2bcfe767203bd..c1b587ce3a6b2 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -61,6 +61,7 @@
     is_timedelta64_dtype,
     needs_i8_conversion,
 )
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.dtypes import PandasDtype
 from pandas.core.dtypes.generic import (
     ABCDatetimeArray,
@@ -1834,17 +1835,18 @@ def union_with_duplicates(lvals: ArrayLike, rvals: ArrayLike) -> ArrayLike:
     -------
     np.ndarray or ExtensionArray
         Containing the unsorted union of both arrays.
+
+    Notes
+    -----
+    Caller is responsible for ensuring lvals.dtype == rvals.dtype.
     """
     indexer = []
     l_count = value_counts(lvals, dropna=False)
     r_count = value_counts(rvals, dropna=False)
     l_count, r_count = l_count.align(r_count, fill_value=0)
-    unique_array = unique(np.append(lvals, rvals))
-    if not isinstance(lvals, np.ndarray):
-        # i.e. ExtensionArray
-        # Note: we only get here with lvals.dtype == rvals.dtype
-        # TODO: are there any cases where union won't be type/dtype preserving?
-        unique_array = type(lvals)._from_sequence(unique_array, dtype=lvals.dtype)
+    unique_array = unique(concat_compat([lvals, rvals]))
+    unique_array = ensure_wrapped_if_datetimelike(unique_array)
+
     for i, value in enumerate(unique_array):
         indexer += [i] * int(max(l_count[value], r_count[value]))
     return unique_array.take(indexer)
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index c7fce9fff3631..28f415476d3fd 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -1,7 +1,10 @@
 """
 Utility functions related to concat.
 """
-from typing import cast
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
 
 import numpy as np
 
@@ -10,7 +13,10 @@
     DtypeObj,
 )
 
-from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.cast import (
+    astype_array,
+    find_common_type,
+)
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_dtype_equal,
@@ -19,15 +25,12 @@
 from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import (
     ABCCategoricalIndex,
+    ABCExtensionArray,
     ABCSeries,
 )
 
-from pandas.core.arrays import ExtensionArray
-from pandas.core.arrays.sparse import SparseArray
-from pandas.core.construction import (
-    array as pd_array,
-    ensure_wrapped_if_datetimelike,
-)
+if TYPE_CHECKING:
+    from pandas.core.arrays.sparse import SparseArray
 
 
 def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
@@ -59,26 +62,11 @@ def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
         # SupportsDType[dtype[Any]], str, Union[Tuple[Any, int], Tuple[Any,
         # Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], _DTypeDict,
         # Tuple[Any, Any]]]"  [arg-type]
-        arr = cast(SparseArray, arr)
+        arr = cast("SparseArray", arr)
         return arr.to_dense().astype(dtype, copy=False)  # type: ignore[arg-type]
 
-    if (
-        isinstance(arr, np.ndarray)
-        and arr.dtype.kind in ["m", "M"]
-        and dtype is np.dtype("object")
-    ):
-        # wrap datetime-likes in EA to ensure astype(object) gives Timestamp/Timedelta
-        # this can happen when concat_compat is called directly on arrays (when arrays
-        # are not coming from Index/Series._values), eg in BlockManager.quantile
-        arr = ensure_wrapped_if_datetimelike(arr)
-
-    if isinstance(dtype, ExtensionDtype):
-        if isinstance(arr, np.ndarray):
-            # numpy's astype cannot handle ExtensionDtypes
-            return pd_array(arr, dtype=dtype, copy=False)
-        return arr.astype(dtype, copy=False)
-
-    return arr.astype(dtype, copy=False)
+    # astype_array includes ensure_wrapped_if_datetimelike
+    return astype_array(arr, dtype=dtype, copy=False)
 
 
 def concat_compat(to_concat, axis: int = 0, ea_compat_axis: bool = False):
@@ -135,7 +123,8 @@ def is_nonempty(x) -> bool:
             target_dtype = find_common_type([x.dtype for x in to_concat])
             to_concat = [cast_to_common_type(arr, target_dtype) for arr in to_concat]
 
-        if isinstance(to_concat[0], ExtensionArray):
+        if isinstance(to_concat[0], ABCExtensionArray):
+            # TODO: what about EA-backed Index?
             cls = type(to_concat[0])
             return cls._concat_same_type(to_concat)
         else:
@@ -346,6 +335,8 @@ def _concat_datetime(to_concat, axis=0):
     -------
     a single array, preserving the combined dtypes
     """
+    from pandas.core.construction import ensure_wrapped_if_datetimelike
+
     to_concat = [ensure_wrapped_if_datetimelike(x) for x in to_concat]
 
     single_dtype = len({x.dtype for x in to_concat}) == 1