de-duplicate using checknull_with_nat

pandas/_libs/missing.pyx

@@ -11,7 +11,7 @@ cnp.import_array()
 cimport util
 
 from tslibs.np_datetime cimport get_timedelta64_value, get_datetime64_value
-from tslibs.nattype cimport NAT
+from tslibs.nattype cimport NAT, checknull_with_nat
 
 cdef float64_t INF = <float64_t>np.inf
 cdef float64_t NEGINF = -INF
@@ -295,9 +295,7 @@ def isneginf_scalar(val: object) -> bool:
 cdef inline bint is_null_datetime64(v):
     # determine if we have a null for a datetime (or integer versions),
     # excluding np.timedelta64('nat')
-    if v is None or util.is_nan(v):
-        return True
-    elif v is NAT:
+    if checknull_with_nat(v):
         return True
     elif util.is_datetime64_object(v):
         return v.view('int64') == NPY_NAT
@@ -307,9 +305,7 @@ cdef inline bint is_null_datetime64(v):
 cdef inline bint is_null_timedelta64(v):
     # determine if we have a null for a timedelta (or integer versions),
     # excluding np.datetime64('nat')
-    if v is None or util.is_nan(v):
-        return True
-    elif v is NAT:
+    if checknull_with_nat(v):
         return True
     elif util.is_timedelta64_object(v):
         return v.view('int64') == NPY_NAT
@@ -319,8 +315,4 @@ cdef inline bint is_null_timedelta64(v):
 cdef inline bint is_null_period(v):
     # determine if we have a null for a Period (or integer versions),
     # excluding np.datetime64('nat') and np.timedelta64('nat')
-    if v is None or util.is_nan(v):
-        return True
-    elif v is NAT:
-        return True
-    return False
+    return checknull_with_nat(v)