**Description**

Allow custom executors subclassed from existing (CeleryExecutor, CeleryKubernetesExecutor, etc.) to use default CLI commands to start workers or flower monitoring.

**Use case / motivation**

Currently, users who decide to roll their own custom Celery-based executor cannot use default commands (i.e. `airflow celery worker`) even though it's built on top of existing CeleryExecutor. If they try to, they'll receive the following error: `airflow command error: argument GROUP_OR_COMMAND: celery subcommand works only with CeleryExecutor, your current executor: custom_package.CustomCeleryExecutor, see help above.`

One workaround for this is to create custom entrypoint script for worker/flower containers/processes that are still going to use the same Celery app as CeleryExecutor. This leads to unnecessary maintaining of this entrypoint script.

I'd suggest two ways of fixing that:

- Check if custom executor is subclassed from Celery executor (which might lead to errors, if custom executor is used to access other celery app, which might be a proper reason for rolling custom executor)
- Store `app` as attribute of Celery-based executors and match the one provided by custom executor with the default one

**Related Issues**

N/A
airflow/cli/cli_parser.py
("-L", "--access-logformat"),
    default=conf.get('webserver', 'ACCESS_LOGFORMAT'),
    help="The access log format for gunicorn logs",
)
ARG_NUM_RUNS = Arg(
    ("-n", "--num-runs"),
    default=conf.getint('scheduler', 'num_runs'),
    type=int,
    help="Set the number of runs to execute before exiting",
)
ARG_DO_PICKLE = Arg(
    ("-p", "--do-pickle"),
    default=False,
    help=(
        "Attempt to pickle the DAG object to send over "
        "to the workers, instead of letting workers run their version "
        "of the code"
    ),
    action="store_true",
)
ARG_QUEUES = Arg(
    ("-q", "--queues"),
    help="Comma delimited list of queues to serve",
    default=conf.get('operators', 'DEFAULT_QUEUE'),
)
ARG_CONCURRENCY = Arg(
    ("-c", "--concurrency"),
    type=int,
    help="The number of worker processes",
    default=conf.get('celery', 'worker_concurrency'),