Currently, the celery executor imports the `Task` model for the Celery database backend directly:

https://github.com/apache/airflow/blob/2dd4e96045d4a7f45cc8c06df3d25c4f1479392c/airflow/executors/celery_executor.py#L38

However, Celery, being highly customisable, uses `self.task_cls` in the backend implementation; this defaults to either `Task` or `TaskExtended` depending on the Celery configuration, see [`celery.backends.database:66`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L66):

> ```python
>     task_cls = Task
> ```

and [`celery.backends.database:76,77`](https://github.com/celery/celery/blob/406f04a082949ac42ec7a4af94fed896c515aaa4/celery/backends/database/__init__.py#L76-L77):

> ```python
>         if self.extended_result:
>             self.task_cls = TaskExtended
> ```

Airflow should support this flexibility, and instead of importing a fixed class just use `backend.task_cls`, so in `_get_many_from_db_backend` just use:

```python
        Task = app.backend.task_cls
        with session_cleanup(session):
            tasks = session.query(Task).filter(Task.task_id.in_(task_ids)).all()
```

I suppose there is one downside to this: `TaskExtended` fetches a few more columns per task, but given that the result of `task.to_dict()` is passed to `backend.meta_from_decoded()`, which could also have been customised to make use of information contained in a custom model, that's neither here nor there. It would be the price anyone using the `result_extended` option would need to pay elsewhere anyway.
airflow/executors/celery_executor.py
pid = os.fork()
    if pid:
        
        pid, ret = os.waitpid(pid, 0)
        if ret == 0:
            return
        raise AirflowException('Celery command failed on host: ' + get_hostname())
    from airflow.sentry import Sentry
    ret = 1
    try:
        from airflow.cli.cli_parser import get_parser
        parser = get_parser()
        
        args = parser.parse_args(command_to_exec[1:])
        setproctitle(f"airflow task supervisor: {command_to_exec}")
        args.func(args)
        ret = 0
    except Exception as e:  
        log.error("Failed to execute task %s.", str(e))
        ret = 1
    finally:
        Sentry.flush()
        os._exit(ret)  
def _execute_in_subprocess(command_to_exec: CommandType) -> None: