BUG: fix+test assigning invalid NAT-like to DTA/TDA/PA

pandas/core/arrays/datetimelike.py

@@ -36,7 +36,7 @@
 )
 from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
 from pandas.core.dtypes.inference import is_array_like
-from pandas.core.dtypes.missing import isna
+from pandas.core.dtypes.missing import is_valid_nat_for_dtype, isna
 
 from pandas._typing import DatetimeLikeScalar
 from pandas.core import missing, nanops
@@ -492,7 +492,10 @@ def __setitem__(
         elif isinstance(value, self._scalar_type):
             self._check_compatible_with(value)
             value = self._unbox_scalar(value)
-        elif isna(value) or value == iNaT:
+        elif is_valid_nat_for_dtype(value, self.dtype):
+            value = iNaT
+        elif not isna(value) and lib.is_integer(value) and value == iNaT:
+            # exclude misc e.g. object() and any NAs not allowed above
             value = iNaT
         else:
             msg = (

pandas/core/dtypes/missing.py

@@ -559,3 +559,27 @@ def remove_na_arraylike(arr):
         return arr[notna(arr)]
     else:
         return arr[notna(lib.values_from_object(arr))]
+
+
+def is_valid_nat_for_dtype(obj, dtype):
+    """
+    isna check that excludes incompatible dtypes
+
+    Parameters
+    ----------
+    obj : object
+    dtype : np.datetime64, np.timedelta64, DatetimeTZDtype, or PeriodDtype
+
+    Returns
+    -------
+    bool
+    """
+    if not isna(obj):
+        return False
+    if dtype.kind == "M":
+        return not isinstance(obj, np.timedelta64)
+    if dtype.kind == "m":
+        return not isinstance(obj, np.datetime64)
+
+    # must be PeriodDType
+    return not isinstance(obj, (np.datetime64, np.timedelta64))

pandas/tests/arrays/test_datetimelike.py

@@ -651,3 +651,51 @@ def test_array_interface(self, period_index):
         result = np.asarray(arr, dtype="S20")
         expected = np.asarray(arr).astype("S20")
         tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array,casting_nats",
+    [
+        (
+            pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            (pd.NaT, np.timedelta64("NaT", "ns")),
+        ),
+        (
+            pd.date_range("2000-01-01", periods=3, freq="D")._data,
+            (pd.NaT, np.datetime64("NaT", "ns")),
+        ),
+        (pd.period_range("2000-01-01", periods=3, freq="D")._data, (pd.NaT,)),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def test_casting_nat_setitem_array(array, casting_nats):
+    expected = type(array)._from_sequence([pd.NaT, array[1], array[2]])
+
+    for nat in casting_nats:
+        arr = array.copy()
+        arr[0] = nat
+        tm.assert_equal(arr, expected)
+
+
+@pytest.mark.parametrize(
+    "array,non_casting_nats",
+    [
+        (
+            pd.TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
+            (np.datetime64("NaT", "ns"),),
+        ),
+        (
+            pd.date_range("2000-01-01", periods=3, freq="D")._data,
+            (np.timedelta64("NaT", "ns"),),
+        ),
+        (
+            pd.period_range("2000-01-01", periods=3, freq="D")._data,
+            (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns")),
+        ),
+    ],
+    ids=lambda x: type(x).__name__,
+)
+def test_invalid_nat_setitem_array(array, non_casting_nats):
+    for nat in non_casting_nats:
+        with pytest.raises(TypeError):
+            array[0] = nat