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
"""
        Overwrite trigger_tasks function from BaseExecutor
        :param open_slots: Number of open slots
        :return:
        """
        sorted_queue = self.order_queued_tasks_by_priority()
        task_tuples_to_send: List[TaskInstanceInCelery] = []
        for _ in range(min(open_slots, len(self.queued_tasks))):
            key, (command, _, queue, simple_ti) = sorted_queue.pop(0)
            task_tuple = (key, simple_ti, command, queue, execute_command)
            task_tuples_to_send.append(task_tuple)
            if key not in self.task_publish_retries:
                self.task_publish_retries[key] = 1
        if task_tuples_to_send:
            self._process_tasks(task_tuples_to_send)
    def _process_tasks(self, task_tuples_to_send: List[TaskInstanceInCelery]) -> None:
        first_task = next(t[4] for t in task_tuples_to_send)
        
        
        cached_celery_backend = first_task.backend
        key_and_async_results = self._send_tasks_to_celery(task_tuples_to_send)
        self.log.debug('Sent all tasks.')
        for key, _, result in key_and_async_results:
            if isinstance(result, ExceptionWithTraceback) and isinstance(
                result.exception, AirflowTaskTimeout
            ):