docs(distributing-expressions): link to pickle docs, generalize UDF kinds

Two fixes in the intro paragraph:

* Link to the standard library pickle docs rather than relying on the
  reader's familiarity with `pickle.dumps` / `pickle.loads`.
* "Python scalar UDFs ride along" only covered scalar UDFs. With
  aggregate and window UDFs now also traveling inline, the line is
  reworded to call out all three kinds.

Also updates the inline code comment in the worker-pool example.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: timsaucer

docs/source/user-guide/io/distributing_expressions.rst

@@ -25,8 +25,10 @@ framework with a per-worker initialization hook), and have each worker
 evaluate the expression against its own slice of data.
 
 DataFusion expressions support this directly: they can be sent through
-:py:mod:`pickle` like any other Python object. Python scalar UDFs ride along
-inside the pickled bytes — the receiver does not need to pre-register them.
+Python's standard `pickle <https://docs.python.org/3/library/pickle.html>`_
+module like any other Python object. Python UDFs — scalar, aggregate, and
+window — travel inside the pickled bytes; the receiver does not need to
+pre-register them.
 
 Basic worker-pool example
 -------------------------
@@ -42,7 +44,7 @@ Basic worker-pool example
 
     def evaluate(blob_and_batch):
         blob, batch = blob_and_batch
-        expr = pickle.loads(blob)  # Python scalar UDFs ride along inline.
+        expr = pickle.loads(blob)  # Python UDFs travel inside the bytes.
         ctx = SessionContext()
         df = ctx.from_pydict({"a": batch})
         return df.with_column("result", expr).select("result").to_pydict()["result"]