diff --git a/bigframes/core/array_value.py b/bigframes/core/array_value.py
index b47637cb59..aac33cf0a6 100644
--- a/bigframes/core/array_value.py
+++ b/bigframes/core/array_value.py
@@ -21,7 +21,6 @@
 import warnings
 
 import google.cloud.bigquery
-import pandas
 import pyarrow as pa
 
 import bigframes.core.expression as ex
@@ -324,10 +323,6 @@ def create_constant(
         value: typing.Any,
         dtype: typing.Optional[bigframes.dtypes.Dtype],
     ) -> Tuple[ArrayValue, str]:
-        if pandas.isna(value):
-            # Need to assign a data type when value is NaN.
-            dtype = dtype or bigframes.dtypes.DEFAULT_DTYPE
-
         return self.project_to_id(ex.const(value, dtype))
 
     def select_columns(
diff --git a/bigframes/dtypes.py b/bigframes/dtypes.py
index 0be31505df..47f137ff1b 100644
--- a/bigframes/dtypes.py
+++ b/bigframes/dtypes.py
@@ -674,14 +674,14 @@ def infer_literal_type(literal) -> typing.Optional[Dtype]:
                 pa.field(key, field_type, nullable=(not pa.types.is_list(field_type)))
             )
         return pd.ArrowDtype(pa.struct(fields))
-    if pd.isna(literal):
-        return None  # Null value without a definite type
     # Make sure to check datetime before date as datetimes are also dates
     if isinstance(literal, (datetime.datetime, pd.Timestamp)):
         if literal.tzinfo is not None:
             return TIMESTAMP_DTYPE
         else:
             return DATETIME_DTYPE
+    if pd.isna(literal):
+        return None  # Null value without a definite type
     from_python_type = _infer_dtype_from_python_type(type(literal))
     if from_python_type is not None:
         return from_python_type
diff --git a/tests/system/small/test_dataframe.py b/tests/system/small/test_dataframe.py
index caf39bd9e9..8d8d9d02a8 100644
--- a/tests/system/small/test_dataframe.py
+++ b/tests/system/small/test_dataframe.py
@@ -3764,6 +3764,12 @@ def test_at_no_duplicate(scalars_df_index, scalars_pandas_df_index):
     assert bf_result == pd_result
 
 
+def test_setitem_w_timestamp_none():
+    b_df = bpd.DataFrame({"rowindex": [1, 2, 3]})
+    b_df["temp_timestamp"] = pd.Timestamp(ts_input=pd.NaT, unit="us", tz="utc")  # type: ignore
+    assert b_df["temp_timestamp"].dtype == "timestamp[us][pyarrow]"
+
+
 def test_loc_setitem_bool_series_scalar_new_col(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     bf_df = scalars_df.copy()