diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 9f111282473c2..b30751fcefe81 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1529,16 +1529,10 @@ def try_datetime(v: np.ndarray) -> ArrayLike:
             # e.g. <class 'numpy.timedelta64'> is not convertible to datetime
             return v.reshape(shape)
         else:
-            if dta.dtype == object or dta.tz is None:
-                # GH#19671 if we have mixed timezones we may have object-dtype
-                #  here.
-                # This is reachable bc allow_object=True, means we cast things
-                #  to mixed-tz datetime objects (mostly).  Only 1 test
-                #  relies on this behavior, see GH#40111
-                # FIXME: conditional reshape is kludgy
-                return np.asarray(dta).reshape(shape)
-            # otherwise we have dt64tz
-            return dta
+            # GH#19761 we may have mixed timezones, in which cast 'dta' is
+            #  an ndarray[object].  Only 1 test
+            #  relies on this behavior, see GH#40111
+            return dta.reshape(shape)
 
     def try_timedelta(v: np.ndarray) -> np.ndarray:
         # safe coerce to timedelta64
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 9a7ae39b9f8eb..814fd389bf644 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -57,7 +57,10 @@
     algorithms,
     common as com,
 )
-from pandas.core.arrays import Categorical
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+)
 from pandas.core.construction import (
     extract_array,
     sanitize_array,
@@ -70,6 +73,10 @@
     union_indexes,
 )
 from pandas.core.internals.array_manager import ArrayManager
+from pandas.core.internals.blocks import (
+    ensure_block_shape,
+    make_block,
+)
 from pandas.core.internals.managers import (
     BlockManager,
     create_block_manager_from_arrays,
@@ -288,13 +295,10 @@ def ndarray_to_mgr(
             # transpose and separate blocks
 
             dvals_list = [maybe_infer_to_datetimelike(row) for row in values]
-            for n in range(len(dvals_list)):
-                if isinstance(dvals_list[n], np.ndarray):
-                    dvals_list[n] = dvals_list[n].reshape(1, -1)
-
-            from pandas.core.internals.blocks import make_block
+            dvals_list = [ensure_block_shape(dval, 2) for dval in dvals_list]
 
             # TODO: What about re-joining object columns?
+            dvals_list = [maybe_squeeze_dt64tz(x) for x in dvals_list]
             block_values = [
                 make_block(dvals_list[n], placement=[n], ndim=2)
                 for n in range(len(dvals_list))
@@ -302,13 +306,25 @@ def ndarray_to_mgr(
 
         else:
             datelike_vals = maybe_infer_to_datetimelike(values)
+            datelike_vals = maybe_squeeze_dt64tz(datelike_vals)
             block_values = [datelike_vals]
     else:
-        block_values = [values]
+        block_values = [maybe_squeeze_dt64tz(values)]
 
     return create_block_manager_from_blocks(block_values, [columns, index])
 
 
+def maybe_squeeze_dt64tz(dta: ArrayLike) -> ArrayLike:
+    """
+    If we have a tzaware DatetimeArray with shape (1, N), squeeze to (N,)
+    """
+    # TODO(EA2D): kludge not needed with 2D EAs
+    if isinstance(dta, DatetimeArray) and dta.ndim == 2 and dta.tz is not None:
+        assert dta.shape[0] == 1
+        dta = dta[0]
+    return dta
+
+
 def dict_to_mgr(
     data: Dict, index, columns, dtype: Optional[DtypeObj], typ: str
 ) -> Manager: