diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 98069e73dfd0c..a61ef62959b14 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1662,7 +1662,12 @@ def take(arr, indices, axis: int = 0, allow_fill: bool = False, fill_value=None)
 
 
 def _take_preprocess_indexer_and_fill_value(
-    arr, indexer, axis, out, fill_value, allow_fill
+    arr: np.ndarray,
+    indexer: Optional[np.ndarray],
+    axis: int,
+    out: Optional[np.ndarray],
+    fill_value,
+    allow_fill: bool,
 ):
     mask_info = None
 
@@ -1783,7 +1788,9 @@ def take_nd(
     return out
 
 
-def take_2d_multi(arr, indexer, fill_value=np.nan):
+def take_2d_multi(
+    arr: np.ndarray, indexer: np.ndarray, fill_value=np.nan
+) -> np.ndarray:
     """
     Specialized Cython take which sets NaN values in one pass.
     """
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 5d9b51238c255..18f1bba6ebfb8 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -35,7 +35,6 @@
     conversion,
     iNaT,
     ints_to_pydatetime,
-    tz_compare,
 )
 from pandas._typing import AnyArrayLike, ArrayLike, Dtype, DtypeObj, Scalar
 from pandas.util._exceptions import find_stack_level
@@ -499,13 +498,13 @@ def ensure_dtype_can_hold_na(dtype: DtypeObj) -> DtypeObj:
     return dtype
 
 
-def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
+def maybe_promote(dtype: np.dtype, fill_value=np.nan):
     """
     Find the minimal dtype that can hold both the given dtype and fill_value.
 
     Parameters
     ----------
-    dtype : np.dtype or ExtensionDtype
+    dtype : np.dtype
     fill_value : scalar, default np.nan
 
     Returns
@@ -567,19 +566,6 @@ def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
                     fill_value = np.timedelta64("NaT", "ns")
                 else:
                     fill_value = fv.to_timedelta64()
-    elif isinstance(dtype, DatetimeTZDtype):
-        if isna(fill_value):
-            fill_value = NaT
-        elif not isinstance(fill_value, datetime):
-            dtype = np.dtype(np.object_)
-        elif fill_value.tzinfo is None:
-            dtype = np.dtype(np.object_)
-        elif not tz_compare(fill_value.tzinfo, dtype.tz):
-            # TODO: sure we want to cast here?
-            dtype = np.dtype(np.object_)
-
-    elif is_extension_array_dtype(dtype) and isna(fill_value):
-        fill_value = dtype.na_value
 
     elif is_float(fill_value):
         if issubclass(dtype.type, np.bool_):
@@ -634,7 +620,7 @@ def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
         if is_float_dtype(dtype) or is_complex_dtype(dtype):
             fill_value = np.nan
         elif is_integer_dtype(dtype):
-            dtype = np.float64
+            dtype = np.dtype(np.float64)
             fill_value = np.nan
         else:
             dtype = np.dtype(np.object_)
@@ -644,9 +630,7 @@ def maybe_promote(dtype: DtypeObj, fill_value=np.nan):
         dtype = np.dtype(np.object_)
 
     # in case we have a string that looked like a number
-    if is_extension_array_dtype(dtype):
-        pass
-    elif issubclass(np.dtype(dtype).type, (bytes, str)):
+    if issubclass(dtype.type, (bytes, str)):
         dtype = np.dtype(np.object_)
 
     fill_value = _ensure_dtype_type(fill_value, dtype)
diff --git a/pandas/tests/dtypes/cast/test_promote.py b/pandas/tests/dtypes/cast/test_promote.py
index 16caf935652cb..08303fc601b3e 100644
--- a/pandas/tests/dtypes/cast/test_promote.py
+++ b/pandas/tests/dtypes/cast/test_promote.py
@@ -7,7 +7,7 @@
 import numpy as np
 import pytest
 
-from pandas._libs.tslibs import NaT, tz_compare
+from pandas._libs.tslibs import NaT
 
 from pandas.core.dtypes.cast import maybe_promote
 from pandas.core.dtypes.common import (
@@ -406,50 +406,6 @@ def test_maybe_promote_any_with_datetime64(
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_datetimetz_with_any_numpy_dtype(
-    tz_aware_fixture, any_numpy_dtype_reduced
-):
-    dtype = DatetimeTZDtype(tz=tz_aware_fixture)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
-
-    # create array of given dtype; casts "1" to correct dtype
-    fill_value = np.array([1], dtype=fill_dtype)[0]
-
-    # filling datetimetz with any numpy dtype casts to object
-    expected_dtype = np.dtype(object)
-    exp_val_for_scalar = fill_value
-
-    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
-
-
-def test_maybe_promote_datetimetz_with_datetimetz(tz_aware_fixture, tz_aware_fixture2):
-    dtype = DatetimeTZDtype(tz=tz_aware_fixture)
-    fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture2)
-
-    # create array of given dtype; casts "1" to correct dtype
-    fill_value = pd.Series([10 ** 9], dtype=fill_dtype)[0]
-
-    # filling datetimetz with datetimetz casts to object, unless tz matches
-    exp_val_for_scalar = fill_value
-    if tz_compare(dtype.tz, fill_dtype.tz):
-        expected_dtype = dtype
-    else:
-        expected_dtype = np.dtype(object)
-
-    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
-
-
-@pytest.mark.parametrize("fill_value", [None, np.nan, NaT])
-def test_maybe_promote_datetimetz_with_na(tz_aware_fixture, fill_value):
-
-    dtype = DatetimeTZDtype(tz=tz_aware_fixture)
-
-    expected_dtype = dtype
-    exp_val_for_scalar = NaT
-
-    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
-
-
 @pytest.mark.parametrize(
     "fill_value",
     [