diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 13fff4d4c9f2b..4af02839d831b 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -3060,6 +3060,9 @@ def dtypes_all_equal(list types not None) -> bool:
     """
     first = types[0]
     for t in types[1:]:
+        if t is first:
+            # Fastpath can provide a nice boost for EADtypes
+            continue
         try:
             if not t == first:
                 return False
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index 9917af9da7665..10b2b6e57c3bf 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -3,10 +3,16 @@
 """
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import (
+    TYPE_CHECKING,
+    Sequence,
+    cast,
+)
 
 import numpy as np
 
+from pandas._libs import lib
+
 from pandas.core.dtypes.astype import astype_array
 from pandas.core.dtypes.cast import (
     common_dtype_categorical_compat,
@@ -26,7 +32,10 @@
 if TYPE_CHECKING:
     from pandas._typing import AxisInt
 
-    from pandas.core.arrays import Categorical
+    from pandas.core.arrays import (
+        Categorical,
+        ExtensionArray,
+    )
 
 
 def concat_compat(to_concat, axis: AxisInt = 0, ea_compat_axis: bool = False):
@@ -38,7 +47,7 @@ def concat_compat(to_concat, axis: AxisInt = 0, ea_compat_axis: bool = False):
 
     Parameters
     ----------
-    to_concat : array of arrays
+    to_concat : sequence of arrays
     axis : axis to provide concatenation
     ea_compat_axis : bool, default False
         For ExtensionArray compat, behave as if axis == 1 when determining
@@ -48,6 +57,24 @@ def concat_compat(to_concat, axis: AxisInt = 0, ea_compat_axis: bool = False):
     -------
     a single array, preserving the combined dtypes
     """
+    if len(to_concat) and lib.dtypes_all_equal([obj.dtype for obj in to_concat]):
+        # fastpath!
+        obj = to_concat[0]
+        if isinstance(obj, np.ndarray):
+            to_concat_arrs = cast("Sequence[np.ndarray]", to_concat)
+            return np.concatenate(to_concat_arrs, axis=axis)
+
+        to_concat_eas = cast("Sequence[ExtensionArray]", to_concat)
+        if ea_compat_axis:
+            # We have 1D objects, that don't support axis keyword
+            return obj._concat_same_type(to_concat_eas)
+        elif axis == 0:
+            return obj._concat_same_type(to_concat_eas)
+        else:
+            # e.g. DatetimeArray
+            # NB: We are assuming here that ensure_wrapped_if_arraylike has
+            #  been called where relevant.
+            return obj._concat_same_type(to_concat_eas, axis=axis)
 
     # filter empty arrays
     # 1-d dtypes always are included here
diff --git a/pandas/core/internals/concat.py b/pandas/core/internals/concat.py
index cfa8b1de594a5..bfdb56147a807 100644
--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -230,10 +230,12 @@ def concatenate_managers(
                 #  we can use np.concatenate, which is more performant
                 #  than concat_compat
                 values = np.concatenate(vals, axis=1)
-            else:
+            elif is_1d_only_ea_dtype(blk.dtype):
                 # TODO(EA2D): special-casing not needed with 2D EAs
-                values = concat_compat(vals, axis=1)
+                values = concat_compat(vals, axis=1, ea_compat_axis=True)
                 values = ensure_block_shape(values, ndim=2)
+            else:
+                values = concat_compat(vals, axis=1)
 
             values = ensure_wrapped_if_datetimelike(values)
 
@@ -541,6 +543,9 @@ def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
                     #  if we did, the missing_arr.fill would cast to gibberish
                     missing_arr = np.empty(self.shape, dtype=empty_dtype)
                     missing_arr.fill(fill_value)
+
+                    if empty_dtype.kind in "mM":
+                        missing_arr = ensure_wrapped_if_datetimelike(missing_arr)
                     return missing_arr
 
             if (not self.indexers) and (not self.block._can_consolidate):