

**Description**

We are using a 3rd party executor which has the ability to use multiple queues, however the `default_queue` is defined under the `celery` heading and is used regardless of the executor that you are using.

See: https://github.com/apache/airflow/blob/2.0.1/airflow/models/baseoperator.py#L366

It would be nice to decouple the default_queue configuration argument away from the celery section to allow other executors to utilise this functionality with less confusion.

**Are you willing to submit a PR?**

Yep! Will open a pull request shortly


**Related Issues**

Not that I'm aware of.

airflow/models/baseoperator.py
is forced to True wherever wait_for_downstream is used. Also note that
        only tasks *immediately* downstream of the previous task instance are waited
        for; the statuses of any tasks further downstream are ignored.
    :type wait_for_downstream: bool
    :param dag: a reference to the dag the task is attached to (if any)
    :type dag: airflow.models.DAG
    :param priority_weight: priority weight of this task against other task.
        This allows the executor to trigger higher priority tasks before
        others when things get backed up. Set priority_weight as a higher
        number for more important tasks.
    :type priority_weight: int
    :param weight_rule: weighting method used for the effective total
        priority weight of the task. Options are:
        ``{ downstream | upstream | absolute }`` default is ``downstream``
        When set to ``downstream`` the effective weight of the task is the
        aggregate sum of all downstream descendants. As a result, upstream
        tasks will have higher weight and will be scheduled more aggressively
        when using positive weight values. This is useful when you have
        multiple dag run instances and desire to have all upstream tasks to
        complete for all runs before each dag can continue processing
        downstream tasks. When set to ``upstream`` the effective weight is the
        aggregate sum of all upstream ancestors. This is the opposite where
        downstream tasks have higher weight and will be scheduled more
        aggressively when using positive weight values. This is useful when you
        have multiple dag run instances and prefer to have each dag complete
        before starting upstream tasks of other dags.  When set to
        ``absolute``, the effective weight is the exact ``priority_weight``
        specified without additional weighting. You may want to do this when
        you know exactly what priority weight each task should have.
        Additionally, when set to ``absolute``, there is bonus effect of