diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index a9fe9d2f864fd..5797528210e8a 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -994,6 +994,14 @@ def _maybe_add_join_keys(
                 else:
                     key_col = Index(lvals).where(~mask_left, rvals)
                     result_dtype = find_common_type([lvals.dtype, rvals.dtype])
+                    if (
+                        lvals.dtype.kind == "M"
+                        and rvals.dtype.kind == "M"
+                        and result_dtype.kind == "O"
+                    ):
+                        # TODO(non-nano) Workaround for common_type not dealing
+                        # with different resolutions
+                        result_dtype = key_col.dtype
 
                 if result._is_label_reference(name):
                     result[name] = result._constructor_sliced(
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index cb773a30901a2..f5a68ae950df2 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -7,7 +7,6 @@
 
 import numpy as np
 import pytest
-import pytz
 
 from pandas.core.dtypes.common import is_object_dtype
 from pandas.core.dtypes.dtypes import CategoricalDtype
@@ -2776,26 +2775,30 @@ def test_merge_arrow_and_numpy_dtypes(dtype):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("tzinfo", [None, pytz.timezone("America/Chicago")])
-def test_merge_datetime_different_resolution(tzinfo):
+@pytest.mark.parametrize("how", ["inner", "left", "outer", "right"])
+@pytest.mark.parametrize("tz", [None, "America/Chicago"])
+def test_merge_datetime_different_resolution(tz, how):
     # https://github.com/pandas-dev/pandas/issues/53200
-    df1 = DataFrame(
-        {
-            "t": [pd.Timestamp(2023, 5, 12, tzinfo=tzinfo, unit="ns")],
-            "a": [1],
-        }
-    )
-    df2 = df1.copy()
+    vals = [
+        pd.Timestamp(2023, 5, 12, tz=tz),
+        pd.Timestamp(2023, 5, 13, tz=tz),
+        pd.Timestamp(2023, 5, 14, tz=tz),
+    ]
+    df1 = DataFrame({"t": vals[:2], "a": [1.0, 2.0]})
+    df1["t"] = df1["t"].dt.as_unit("ns")
+    df2 = DataFrame({"t": vals[1:], "b": [1.0, 2.0]})
     df2["t"] = df2["t"].dt.as_unit("s")
 
-    expected = DataFrame(
-        {
-            "t": [pd.Timestamp(2023, 5, 12, tzinfo=tzinfo)],
-            "a_x": [1],
-            "a_y": [1],
-        }
-    )
-    result = df1.merge(df2, on="t")
+    expected = DataFrame({"t": vals, "a": [1.0, 2.0, np.nan], "b": [np.nan, 1.0, 2.0]})
+    expected["t"] = expected["t"].dt.as_unit("ns")
+    if how == "inner":
+        expected = expected.iloc[[1]].reset_index(drop=True)
+    elif how == "left":
+        expected = expected.iloc[[0, 1]]
+    elif how == "right":
+        expected = expected.iloc[[1, 2]].reset_index(drop=True)
+
+    result = df1.merge(df2, on="t", how=how)
     tm.assert_frame_equal(result, expected)