diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index 3dc7acc6cf0b5..5f67db0244a4a 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -481,6 +481,7 @@ def sanitize_array(
         return subarr
 
     elif isinstance(data, (list, tuple, abc.Set, abc.ValuesView)) and len(data) > 0:
+        # TODO: deque, array.array
         if isinstance(data, set):
             # Raise only for unordered sets, e.g., not for dict_keys
             raise TypeError("Set type is unordered")
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 034fd927a8017..1725a8de53e67 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1339,6 +1339,9 @@ def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
     from pandas.core.tools.datetimes import to_datetime
     from pandas.core.tools.timedeltas import to_timedelta
 
+    if not is_list_like(value):
+        raise TypeError("value must be listlike")
+
     if dtype is not None:
         is_datetime64 = is_datetime64_dtype(dtype)
         is_datetime64tz = is_datetime64tz_dtype(dtype)
@@ -1367,13 +1370,6 @@ def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
                         raise TypeError(
                             f"cannot convert datetimelike to dtype [{dtype}]"
                         )
-            elif is_datetime64tz:
-
-                # our NaT doesn't support tz's
-                # this will coerce to DatetimeIndex with
-                # a matching dtype below
-                if is_scalar(value) and isna(value):
-                    value = [value]
 
             elif is_timedelta64 and not is_dtype_equal(dtype, TD64NS_DTYPE):
 
@@ -1386,9 +1382,7 @@ def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
                 else:
                     raise TypeError(f"cannot convert timedeltalike to dtype [{dtype}]")
 
-            if is_scalar(value):
-                value = maybe_unbox_datetimelike(value, dtype)
-            elif not is_sparse(value):
+            if not is_sparse(value):
                 value = np.array(value, copy=False)
 
                 # have a scalar array-like (e.g. NaT)