### 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)

self.start_date = start_date
        self.end_date = end_date
        self.try_number = try_number
        self.state = state
        self.executor_config = executor_config
        self.run_as_user = run_as_user
        self.pool = pool
        self.priority_weight = priority_weight
        self.queue = queue
        self.key = key
    def __eq__(self, other):
        if isinstance(other, self.__class__):
            return self.__dict__ == other.__dict__
        return NotImplemented
    def as_dict(self):
        warnings.warn(
            "This method is deprecated. Use BaseSerialization.serialize.",
            RemovedInAirflow3Warning,
            stacklevel=2,
        )
        new_dict = dict(self.__dict__)
        for key in new_dict:
            if key in ["start_date", "end_date"]:
                val = new_dict[key]
                if not val or isinstance(val, str):
                    continue
                new_dict.update({key: val.isoformat()})
        return new_dict
    @classmethod
    def from_ti(cls, ti: TaskInstance) -> SimpleTaskInstance: