[dask] Use distributed.MultiLock (#6743)

* [dask] Use `distributed.MultiLock`

This enables training multiple models in parallel.

* Conditionally import `MultiLock`.
* Use async train directly in scikit learn interface.
* Use `worker_client` when available.

tests/python/test_tracker.py

@@ -27,7 +27,7 @@ def run_rabit_ops(client, n_workers):
     from xgboost.dask import RabitContext, _get_rabit_args
     from xgboost import rabit
 
-    workers = list(_get_client_workers(client).keys())
+    workers = _get_client_workers(client)
     rabit_args = client.sync(_get_rabit_args, len(workers), client)
     assert not rabit.is_distributed()
     n_workers_from_dask = len(workers)