### 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)

"""
        Return the try number that this task number will be when it is actually
        run.
        If the TaskInstance is currently running, this will match the column in the
        database, in all other cases this will be incremented.
        """
        
        if self.state == State.RUNNING:
            return self._try_number
        return self._try_number + 1
    @try_number.setter
    def try_number(self, value: int) -> None:
        self._try_number = value
    @property
    def prev_attempted_tries(self) -> int:
        """
        Based on this instance's try_number, this will calculate
        the number of previously attempted tries, defaulting to 0.
        """
        
        
        
        
        
        return self._try_number
    @property
    def next_try_number(self) -> int: