regression fix for merging DF with datetime index with empty DF

doc/source/whatsnew/v1.2.0.rst

@@ -467,7 +467,7 @@ MultiIndex
 
 - Bug in :meth:`DataFrame.xs` when used with :class:`IndexSlice` raises ``TypeError`` with message ``"Expected label or tuple of labels"`` (:issue:`35301`)
 - Bug in :meth:`DataFrame.reset_index` with ``NaT`` values in index raises ``ValueError`` with message ``"cannot convert float NaN to integer"`` (:issue:`36541`)
--
+- Fixed regression in :func:`merge` on merging DatetimeIndex with empty DataFrame (:issue:`36895`)
 
 I/O
 ^^^

pandas/core/reshape/merge.py

@@ -830,12 +830,15 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
                     rvals = algos.take_1d(take_right, right_indexer, fill_value=rfill)
 
                 # if we have an all missing left_indexer
-                # make sure to just use the right values
-                mask = left_indexer == -1
-                if mask.all():
+                # make sure to just use the right values or vice-versa
+                mask_left = left_indexer == -1
+                mask_right = right_indexer == -1
+                if mask_left.all():
                     key_col = rvals
+                elif mask_right.all():
+                    key_col = lvals
                 else:
-                    key_col = Index(lvals).where(~mask, rvals)
+                    key_col = Index(lvals).where(~mask_left, rvals)
 
                 if result._is_label_reference(name):
                     result[name] = key_col

pandas/tests/reshape/merge/test_multi.py

@@ -481,6 +481,56 @@ def test_merge_datetime_index(self, klass):
         result = df.merge(df, on=[df.index.year], how="inner")
         tm.assert_frame_equal(result, expected)
 
+    def test_merge_datetime_multi_index_empty_df(self):
+
+        midx1 = pd.MultiIndex.from_tuples(
+            [[pd.Timestamp("1950-01-01"), "A"], [pd.Timestamp("1950-01-02"), "B"]],
+            names=["date", "panel"],
+        )
+        left = DataFrame(
+            data={
+                "data": [1.5, 1.5],
+            },
+            index=midx1,
+        )
+
+        midx2 = pd.MultiIndex.from_tuples([], names=["date", "panel"])
+
+        right = DataFrame(index=midx2, columns=["state"])
+
+        midx3 = pd.MultiIndex.from_tuples(
+            [[pd.Timestamp("1950-01-01"), "A"], [pd.Timestamp("1950-01-02"), "B"]],
+            names=["date", "panel"],
+        )
+
+        expected_left_merge = DataFrame(
+            data={
+                "data": [1.5, 1.5],
+                "state": [None, None],
+            },
+            index=midx3,
+        )
+
+        expected_right_merge = DataFrame(
+            data={
+                "state": [None, None],
+                "data": [1.5, 1.5],
+            },
+            index=midx3,
+        )
+
+        result_left_merge = left.merge(right, how="left", on=["date", "panel"])
+        tm.assert_frame_equal(result_left_merge, expected_left_merge)
+
+        result_right_merge = right.merge(left, how="right", on=["date", "panel"])
+        tm.assert_frame_equal(result_right_merge, expected_right_merge)
+
+        result_left_join = left.join(right, how="left")
+        tm.assert_frame_equal(result_left_join, expected_left_merge)
+
+        result_right_join = right.join(left, how="right")
+        tm.assert_frame_equal(result_right_join, expected_right_merge)
+
     def test_join_multi_levels(self):
 
         # GH 3662