diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index 71e6d14e6a716..dd02771f735a6 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -274,9 +274,6 @@ def apply(
         else:
             new_axes = self._axes
 
-        if len(result_arrays) == 0:
-            return self.make_empty(new_axes)
-
         # error: Argument 1 to "ArrayManager" has incompatible type "List[ndarray]";
         # expected "List[Union[ndarray, ExtensionArray]]"
         return type(self)(result_arrays, new_axes)  # type: ignore[arg-type]
@@ -487,7 +484,7 @@ def _get_data_subset(self: T, predicate: Callable) -> T:
         indices = [i for i, arr in enumerate(self.arrays) if predicate(arr)]
         arrays = [self.arrays[i] for i in indices]
         # TODO copy?
-        new_axes = [self._axes[0], self._axes[1][np.array(indices, dtype="int64")]]
+        new_axes = [self._axes[0], self._axes[1][np.array(indices, dtype="intp")]]
         return type(self)(arrays, new_axes, verify_integrity=False)
 
     def get_bool_data(self: T, copy: bool = False) -> T:
@@ -696,7 +693,6 @@ def _equal_values(self, other) -> bool:
             return True
 
     # TODO
-    # equals
     # to_dict
 
 
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 73f463997c085..cdb7d8a6ccd45 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -345,9 +345,6 @@ def apply(
         if ignore_failures:
             return self._combine(result_blocks)
 
-        if len(result_blocks) == 0:
-            return self.make_empty(self.axes)
-
         return type(self).from_blocks(result_blocks, self.axes)
 
     def where(self: T, other, cond, align: bool, errors: str) -> T:
@@ -532,6 +529,13 @@ def _combine(
     ) -> T:
         """ return a new manager with the blocks """
         if len(blocks) == 0:
+            if self.ndim == 2:
+                # retain our own Index dtype
+                if index is not None:
+                    axes = [self.items[:0], index]
+                else:
+                    axes = [self.items[:0]] + self.axes[1:]
+                return self.make_empty(axes)
             return self.make_empty()
 
         # FIXME: optimization potential
@@ -1233,7 +1237,7 @@ def grouped_reduce(self: T, func: Callable, ignore_failures: bool = False) -> T:
             index = Index(range(result_blocks[0].values.shape[-1]))
 
         if ignore_failures:
-            return self._combine(result_blocks, index=index)
+            return self._combine(result_blocks, copy=False, index=index)
 
         return type(self).from_blocks(result_blocks, [self.axes[0], index])
 
@@ -1270,7 +1274,7 @@ def reduce(
                 new_mgr = self._combine(res_blocks, copy=False, index=index)
             else:
                 indexer = []
-                new_mgr = type(self).from_blocks([], [Index([]), index])
+                new_mgr = type(self).from_blocks([], [self.items[:0], index])
         else:
             indexer = np.arange(self.shape[0])
             new_mgr = type(self).from_blocks(res_blocks, [self.items, index])
diff --git a/pandas/tests/generic/test_generic.py b/pandas/tests/generic/test_generic.py
index d07f843f4acfc..771d31aa6865b 100644
--- a/pandas/tests/generic/test_generic.py
+++ b/pandas/tests/generic/test_generic.py
@@ -85,7 +85,7 @@ def test_rename(self):
 
         # multiple axes at once
 
-    def test_get_numeric_data(self, using_array_manager):
+    def test_get_numeric_data(self):
 
         n = 4
         kwargs = {
@@ -100,9 +100,9 @@ def test_get_numeric_data(self, using_array_manager):
         # non-inclusion
         result = o._get_bool_data()
         expected = self._construct(n, value="empty", **kwargs)
-        if using_array_manager and isinstance(o, DataFrame):
-            # INFO(ArrayManager) preserve the dtype of the columns Index
-            expected.columns = expected.columns.astype("int64")
+        if isinstance(o, DataFrame):
+            # preserve columns dtype
+            expected.columns = o.columns[:0]
         self._compare(result, expected)
 
         # get the bool data