diff --git a/pandas/_testing/__init__.py b/pandas/_testing/__init__.py
index f5f2aa23459e4..25da4f8eb8278 100644
--- a/pandas/_testing/__init__.py
+++ b/pandas/_testing/__init__.py
@@ -296,7 +296,7 @@ def box_expected(expected, box_cls, transpose: bool = True):
             # pd.array would return an IntegerArray
             expected = PandasArray(np.asarray(expected._values))
         else:
-            expected = pd.array(expected)
+            expected = pd.array(expected, copy=False)
     elif box_cls is Index:
         expected = Index._with_infer(expected)
     elif box_cls is Series:
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 83c7fa2fbdca1..500adb316adf0 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -1259,8 +1259,8 @@ def _add_timedeltalike_scalar(self, other):
             return type(self)._simple_new(new_values, dtype=self.dtype)
 
         # PeriodArray overrides, so we only get here with DTA/TDA
-        # error: "DatetimeLikeArrayMixin" has no attribute "_reso"
-        inc = delta_to_nanoseconds(other, reso=self._reso)  # type: ignore[attr-defined]
+        self = cast("DatetimeArray | TimedeltaArray", self)
+        inc = delta_to_nanoseconds(other, reso=self._reso)
 
         new_values = checked_add_with_arr(self.asi8, inc, arr_mask=self._isnan)
         new_values = new_values.view(self._ndarray.dtype)
@@ -1270,10 +1270,7 @@ def _add_timedeltalike_scalar(self, other):
             # adding a scalar preserves freq
             new_freq = self.freq
 
-        # error: Unexpected keyword argument "freq" for "_simple_new" of "NDArrayBacked"
-        return type(self)._simple_new(  # type: ignore[call-arg]
-            new_values, dtype=self.dtype, freq=new_freq
-        )
+        return type(self)._simple_new(new_values, dtype=self.dtype, freq=new_freq)
 
     def _add_timedelta_arraylike(
         self, other: TimedeltaArray | npt.NDArray[np.timedelta64]
@@ -2217,11 +2214,11 @@ def validate_periods(periods: None) -> None:
 
 
 @overload
-def validate_periods(periods: float) -> int:
+def validate_periods(periods: int | float) -> int:
     ...
 
 
-def validate_periods(periods: float | None) -> int | None:
+def validate_periods(periods: int | float | None) -> int | None:
     """
     If a `periods` argument is passed to the Datetime/Timedelta Array/Index
     constructor, cast it to an integer.
@@ -2244,9 +2241,8 @@ def validate_periods(periods: float | None) -> int | None:
             periods = int(periods)
         elif not lib.is_integer(periods):
             raise TypeError(f"periods must be a number, got {periods}")
-    # error: Incompatible return value type (got "Optional[float]",
-    # expected "Optional[int]")
-    return periods  # type: ignore[return-value]
+        periods = cast(int, periods)
+    return periods
 
 
 def validate_inferred_freq(
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 75a0db3233130..966eca9cc7952 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -220,7 +220,7 @@ def _maybe_unbox_datetimelike(value: Scalar, dtype: DtypeObj) -> Scalar:
     """
     if is_valid_na_for_dtype(value, dtype):
         # GH#36541: can't fill array directly with pd.NaT
-        # > np.empty(10, dtype="datetime64[64]").fill(pd.NaT)
+        # > np.empty(10, dtype="datetime64[ns]").fill(pd.NaT)
         # ValueError: cannot convert float NaN to integer
         value = dtype.type("NaT", "ns")
     elif isinstance(value, Timestamp):
@@ -800,6 +800,8 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> tuple[DtypeObj,
         if val is NaT or val.tz is None:  # type: ignore[comparison-overlap]
             dtype = np.dtype("M8[ns]")
             val = val.to_datetime64()
+            # TODO(2.0): this should be dtype = val.dtype
+            #  to get the correct M8 resolution
         else:
             if pandas_dtype:
                 dtype = DatetimeTZDtype(unit="ns", tz=val.tz)