**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
ARG_UPSTREAM,
            ARG_DOWNSTREAM,
            ARG_YES,
            ARG_ONLY_FAILED,
            ARG_ONLY_RUNNING,
            ARG_EXCLUDE_SUBDAGS,
            ARG_EXCLUDE_PARENTDAG,
            ARG_DAG_REGEX,
        ),
    ),
    ActionCommand(
        name='state',
        help="Get the status of a task instance",
        func=lazy_load_command('airflow.cli.commands.task_command.task_state'),
        args=(ARG_DAG_ID, ARG_TASK_ID, ARG_EXECUTION_DATE_OR_DAGRUN_ID, ARG_SUBDIR, ARG_VERBOSE),
    ),
    ActionCommand(
        name='failed-deps',
        help="Returns the unmet dependencies for a task instance",
        description=(
            "Returns the unmet dependencies for a task instance from the perspective of the scheduler. "
            "In other words, why a task instance doesn't get scheduled and then queued by the scheduler, "
            "and then run by an executor."
        ),
        func=lazy_load_command('airflow.cli.commands.task_command.task_failed_deps'),
        args=(ARG_DAG_ID, ARG_TASK_ID, ARG_EXECUTION_DATE_OR_DAGRUN_ID, ARG_SUBDIR),
    ),
    ActionCommand(
        name='render',
        help="Render a task instance's template(s)",