### Description

expanding tasks in batches to allow mapped tasks spawn more than 1024 processes.

### Use case/motivation

Maximum length of a list is limited to 1024 by `max_map_length (AIRFLOW__CORE__MAX_MAP_LENGTH)`.
during scheduling of the new tasks, an UPDATE query is ran that tries to set all the new tasks at once.  Increasing `max_map_length` more than 4K makes airflow scheduler completely unresponsive.

Also, Postgres throws `stack depth limit exceeded` error which can be fixed by updating to a newer version and setting `max_stack_depth` higher. But it doesn't really matter because airflow scheduler freezes up.
 
As a workaround, I split the dag runs into subdag runs which works but it would be much nicer if we didn't have to worry about exceeding  `max_map_length`. 

### Related issues

It was discussed here:
[Increasing 'max_map_length' leads to SQL 'max_stack_depth' error with 5000 dags to be spawned #28478](https://github.com/apache/airflow/discussions/28478)

### Are you willing to submit a PR?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

"""
        Get datetime of the next retry if the task instance fails. For exponential
        backoff, retry_delay is used as base and will be converted to seconds.
        """
        from airflow.models.abstractoperator import MAX_RETRY_DELAY
        delay = self.task.retry_delay
        if self.task.retry_exponential_backoff:
            
            
            
            min_backoff = int(math.ceil(delay.total_seconds() * (2 ** (self.try_number - 2))))
            
            
            
            
            if min_backoff < 1:
                min_backoff = 1
            
            ti_hash = int(
                hashlib.sha1(
                    f"{self.dag_id}#{self.task_id}#{self.execution_date}#{self.try_number}".encode()
                ).hexdigest(),
                16,
            )
            
            modded_hash = min_backoff + ti_hash % min_backoff