[SPARK-22541][SQL] Explicitly claim that Python udfs can't be conditionally executed with short-curcuit evaluation

## What changes were proposed in this pull request?

Besides conditional expressions such as `when` and `if`, users may want to conditionally execute python udfs by short-curcuit evaluation. We should also explicitly note that python udfs don't support this kind of conditional execution too.

## How was this patch tested?

N/A, just document change.

Author: Liang-Chi Hsieh <[email protected]>

Closes #19787 from viirya/SPARK-22541.

Author: viirya

python/pyspark/sql/functions.py

@@ -2079,12 +2079,9 @@ def udf(f=None, returnType=StringType()):
         duplicate invocations may be eliminated or the function may even be invoked more times than
         it is present in the query.
 
-    .. note:: The user-defined functions do not support conditional execution by using them with
-        SQL conditional expressions such as `when` or `if`. The functions still apply on all rows no
-        matter the conditions are met or not. So the output is correct if the functions can be
-        correctly run on all rows without failure. If the functions can cause runtime failure on the
-        rows that do not satisfy the conditions, the suggested workaround is to incorporate the
-        condition logic into the functions.
+    .. note:: The user-defined functions do not support conditional expressions or short curcuiting
+        in boolean expressions and it ends up with being executed all internally. If the functions
+        can fail on special rows, the workaround is to incorporate the condition into the functions.
 
     :param f: python function if used as a standalone function
     :param returnType: a :class:`pyspark.sql.types.DataType` object
@@ -2194,12 +2191,9 @@ def pandas_udf(f=None, returnType=None, functionType=None):
 
     .. note:: The user-defined function must be deterministic.
 
-    .. note:: The user-defined functions do not support conditional execution by using them with
-        SQL conditional expressions such as `when` or `if`. The functions still apply on all rows no
-        matter the conditions are met or not. So the output is correct if the functions can be
-        correctly run on all rows without failure. If the functions can cause runtime failure on the
-        rows that do not satisfy the conditions, the suggested workaround is to incorporate the
-        condition logic into the functions.
+    .. note:: The user-defined functions do not support conditional expressions or short curcuiting
+        in boolean expressions and it ends up with being executed all internally. If the functions
+        can fail on special rows, the workaround is to incorporate the condition into the functions.
     """
     # decorator @pandas_udf(returnType, functionType)
     is_decorator = f is None or isinstance(f, (str, DataType))