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
task_ids = _tasks_list_to_task_ids(async_tasks)
        keys = [app.backend.get_key_for_task(k) for k in task_ids]
        values = app.backend.mget(keys)
        task_results = [app.backend.decode_result(v) for v in values if v]
        task_results_by_task_id = {task_result["task_id"]: task_result for task_result in task_results}
        return self._prepare_state_and_info_by_task_dict(task_ids, task_results_by_task_id)
    def _get_many_from_db_backend(self, async_tasks) -> Mapping[str, EventBufferValueType]:
        task_ids = _tasks_list_to_task_ids(async_tasks)
        session = app.backend.ResultSession()
        with session_cleanup(session):
            tasks = session.query(TaskDb).filter(TaskDb.task_id.in_(task_ids)).all()
        task_results = [app.backend.meta_from_decoded(task.to_dict()) for task in tasks]
        task_results_by_task_id = {task_result["task_id"]: task_result for task_result in task_results}
        return self._prepare_state_and_info_by_task_dict(task_ids, task_results_by_task_id)
    @staticmethod
    def _prepare_state_and_info_by_task_dict(
        task_ids, task_results_by_task_id
    ) -> Mapping[str, EventBufferValueType]:
        state_info: MutableMapping[str, EventBufferValueType] = {}
        for task_id in task_ids:
            task_result = task_results_by_task_id.get(task_id)
            if task_result:
                state = task_result["status"]
                info = None if not hasattr(task_result, "info") else task_result["info"]
            else:
                state = celery_states.PENDING
                info = None
            state_info[task_id] = state, info
        return state_info
    def _get_many_using_multiprocessing(self, async_results) -> Mapping[str, EventBufferValueType]: