BUG: series from dict of Timedelta scalar drops nanoseconds

reworking

Author: ma3da

pandas/core/dtypes/cast.py

@@ -88,7 +88,12 @@
     ABCSeries,
 )
 from pandas.core.dtypes.inference import is_list_like
-from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
+from pandas.core.dtypes.missing import (
+    is_valid_nat_for_dtype,
+    isna,
+    na_value_for_dtype,
+    notna,
+)
 
 if TYPE_CHECKING:
     from pandas import Series
@@ -1693,9 +1698,13 @@ def construct_1d_arraylike_from_scalar(
             if not isna(value):
                 value = ensure_str(value)
         elif dtype.kind in ["M", "m"]:
-            # GH36541: can't fill array directly with pd.NaT -> ValueError
-            # GH38032: filling in pd.Timedelta loses nanoseconds
-            value = convert_scalar_for_putitemlike(value, dtype)
+            # GH38032: filling in Timedelta/Timestamp drops nanoseconds
+            if isinstance(value, (Timedelta, Timestamp)):
+                value = value.to_numpy()
+            # GH36541: filling datetime-like array directly with pd.NaT
+            # raises ValueError: cannot convert float NaN to integer
+            elif is_valid_nat_for_dtype(value, dtype):
+                value = np.datetime64("NaT")
 
         subarr = np.empty(length, dtype=dtype)
         subarr.fill(value)