BUG: preserve DTA/TDA+timedeltalike scalar with mismatched resos

pandas/_libs/tslibs/timedeltas.pyx

@@ -1926,6 +1926,7 @@ class Timedelta(_Timedelta):
     def __floordiv__(self, other):
         # numpy does not implement floordiv for timedelta64 dtype, so we cannot
         # just defer
+        orig = other
         if _should_cast_to_timedelta(other):
             # We interpret NaT as timedelta64("NaT")
             other = Timedelta(other)

pandas/core/arrays/datetimelike.py

@@ -1275,7 +1275,9 @@ def _add_timedeltalike_scalar(self, other):
 
         # PeriodArray overrides, so we only get here with DTA/TDA
         self = cast("DatetimeArray | TimedeltaArray", self)
-        other = Timedelta(other)._as_unit(self._unit)
+        other = Timedelta(other)
+
+        self, other = self._ensure_matching_resos(other)
         return self._add_timedeltalike(other)
 
     def _add_timedelta_arraylike(self, other: TimedeltaArray):

pandas/core/construction.py

@@ -325,6 +325,13 @@ def array(
 
     data = extract_array(data, extract_numpy=True)
 
+    if isinstance(data, ExtensionArray) and (
+        dtype is None or is_dtype_equal(dtype, data.dtype)
+    ):
+        if copy:
+            return data.copy()
+        return data
+
     # this returns None for not-found dtypes.
     if isinstance(dtype, str):
         dtype = registry.find(dtype) or dtype

pandas/tests/arrays/test_datetimes.py

@@ -1,13 +1,17 @@
 """
 Tests for DatetimeArray
 """
+from datetime import timedelta
 import operator
 
 import numpy as np
 import pytest
 
 from pandas._libs.tslibs import tz_compare
-from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas._libs.tslibs.dtypes import (
+    NpyDatetimeUnit,
+    npy_unit_to_abbrev,
+)
 
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 
@@ -221,6 +225,35 @@ def test_add_mismatched_reso_doesnt_downcast(self):
         #  (so we _could_ downcast to unit="s"), we do not.
         assert res._unit == "us"
 
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            timedelta(hours=2),
+            pd.Timedelta(hours=2),
+            np.timedelta64(2, "h"),
+            np.timedelta64(2 * 3600 * 1000, "ms"),
+            pd.offsets.Minute(120),
+            pd.offsets.Hour(2),
+        ],
+    )
+    def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar):
+        dta, dti = dta_dti
+
+        td = pd.Timedelta(scalar)
+        exp_reso = max(dta._reso, td._reso)
+        exp_unit = npy_unit_to_abbrev(exp_reso)
+
+        expected = (dti + td)._data._as_unit(exp_unit)
+        result = dta + scalar
+        tm.assert_extension_array_equal(result, expected)
+
+        result = scalar + dta
+        tm.assert_extension_array_equal(result, expected)
+
+        expected = (dti - td)._data._as_unit(exp_unit)
+        result = dta - scalar
+        tm.assert_extension_array_equal(result, expected)
+
     def test_sub_datetimelike_scalar_mismatch(self):
         dti = pd.date_range("2016-01-01", periods=3)
         dta = dti._data._as_unit("us")

pandas/tests/scalar/timedelta/test_timedelta.py

@@ -183,7 +183,8 @@ def test_truediv_timedeltalike(self, td):
         assert (2.5 * td) / td == 2.5
 
         other = Timedelta(td.value)
-        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow."
+
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow"
         with pytest.raises(OutOfBoundsTimedelta, match=msg):
             td / other
 

pandas/tests/tseries/offsets/test_offsets.py

@@ -576,8 +576,13 @@ def test_add_dt64_ndarray_non_nano(self, offset_types, unit, request):
             )
             request.node.add_marker(mark)
 
+            # result.dtype should match M8[{unit}], while expected.dtype should
+            #  always be in nanos
+            #  (this is for cases when offset_types is not itself Nano)
+            assert result.dtype != expected.dtype
+
         tm.assert_numpy_array_equal(
-            result._ndarray, expected._ndarray.astype(arr.dtype)
+            result._ndarray, expected._ndarray.astype(result.dtype)
         )