**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
help="Start a scheduler instance",
        func=lazy_load_command('airflow.cli.commands.scheduler_command.scheduler'),
        args=(
            ARG_SUBDIR,
            ARG_NUM_RUNS,
            ARG_DO_PICKLE,
            ARG_PID,
            ARG_DAEMON,
            ARG_STDOUT,
            ARG_STDERR,
            ARG_LOG_FILE,
            ARG_SKIP_SERVE_LOGS,
        ),
        epilog=(
            'Signals:\n'
            '\n'
            '  - SIGUSR2: Dump a snapshot of task state being tracked by the executor.\n'
            '\n'
            '    Example:\n'
            '        pkill -f -USR2 "airflow scheduler"'
        ),
    ),
    ActionCommand(
        name='triggerer',
        help="Start a triggerer instance",
        func=lazy_load_command('airflow.cli.commands.triggerer_command.triggerer'),
        args=(
            ARG_PID,
            ARG_DAEMON,
            ARG_STDOUT,