**Description**

This operator should cancel running workflow template `on_kill`. This option probably should be configurable because of request_id prameter in a job definition: https://googleapis.dev/python/dataproc/latest/gapic/v1/api.html#google.cloud.dataproc_v1.WorkflowTemplateServiceClient.instantiate_inline_workflow_template
https://googleapis.dev/python/dataproc/latest/gapic/v1/api.html#google.cloud.dataproc_v1.WorkflowTemplateServiceClient.instantiate_workflow_template

Note this will have to cancle the Google Long Running Operation for the workflow template (which will in turn cancel all children jobs and clean up relevant cluster).

**Use case / motivation**

Remove dangling workflow templates

**Related Issues**
#10381
#6371
call.hook().update_cluster(**update_cluster_args),
        ]
        op = DataprocScaleClusterOperator(
            task_id=TASK_ID,
            cluster_name=CLUSTER_NAME,
            project_id=GCP_PROJECT,
            region=GCP_REGION,
            num_workers=3,
            num_preemptible_workers=4,
            graceful_decommission_timeout="10m",
            gcp_conn_id=GCP_CONN_ID,
            impersonation_chain=IMPERSONATION_CHAIN,
        )
        op.execute(context=self.mock_context)
        mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
        mock_hook.return_value.update_cluster.assert_called_once_with(**update_cluster_args)
        
        self.extra_links_manager_mock.assert_has_calls(expected_calls, any_order=False)
        self.mock_ti.xcom_push.assert_called_once_with(
            key="conf",
            value=DATAPROC_CLUSTER_CONF_EXPECTED,
            execution_date=None,
        )
@pytest.mark.need_serialized_dag
def test_scale_cluster_operator_extra_links(dag_maker, create_task_instance_of_operator):
    ti = create_task_instance_of_operator(
        DataprocScaleClusterOperator,
        dag_id=TEST_DAG_ID,
        execution_date=DEFAULT_DATE,
        task_id=TASK_ID,