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**Description**

Related to the discussion here: https://github.com/apache/airflow/pull/8553#discussion_r416136954

Allow user to define expected terminal state. e.g. `JOB_STATE_CANCELLED` could be not treated as failure but expected terminal state.


<!-- A short description of your feature -->


https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
    :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
        successfully cancelled when task is being killed.
    :param wait_until_finished: (Optional)
        If True, wait for the end of pipeline execution before exiting.
        If False, only submits job.
        If None, default behavior.
        The default behavior depends on the type of pipeline:
        * for the streaming pipeline, wait for jobs to start,
        * for the batch pipeline, wait for the jobs to complete.
        .. warning::
            You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
            to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
            always wait until finished. For more information, look at:
            `Asynchronous execution
            <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
        The process of starting the Dataflow job in Airflow consists of two steps:
        * running a subprocess and reading the stderr/stderr log for the job id.
        * loop waiting for the end of the job ID from the previous step.
          This loop checks the status of the job.
        Step two is started just after step one has finished, so if you have wait_until_finished in your
        pipeline code, step two will not start until the process stops. When this process stops,
        steps two will run, but it will only execute one iteration as the job will be in a terminal state.
        If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
        to the operator, the second loop will wait for the job's terminal state.
        If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
        to the operator, the second loop will check once is job not in terminal state and exit the loop.
    """
    template_fields: Sequence[str] = ("options", "dataflow_default_options", "job_name", "py_file")
    def __init__(