### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Our scheduler started failing with this trace:

  File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1187, in get_failed_dep_statuses
    for dep_status in dep.get_dep_statuses(self, session, dep_context):
  File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/base_ti_dep.py", line 95, in get_dep_statuses
    yield from self._get_dep_statuses(ti, session, dep_context)
  File "/usr/local/lib/python3.9/site-packages/airflow/ti_deps/deps/not_in_retry_period_dep.py", line 47, in _get_dep_statuses
    next_task_retry_date = ti.next_retry_datetime()
  File "/usr/local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1243, in next_retry_datetime
    return self.end_date + delay
OverflowError: date value out of range

We found a dag with a large # of retries and exponential backoff will trigger this date error and take down the entire scheduler. The workaround is to force a max_delay setting. 

The bug is here:
https://github.com/apache/airflow/blob/2.3.3/airflow/models/taskinstance.py#L1243

The current version seems to use the same code:
https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py#L1147


### What you think should happen instead

There are a few solutions. Exponential backoff should probably require a max delay value.

At the very least, it shouldn't kill the scheduler.

### How to reproduce

Create dag with exponential delay and force it to retry until it overflows.

### Operating System

linux

### Versions of Apache Airflow Providers

_No response_

### Deployment

Astronomer

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit 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)

if TYPE_CHECKING:
                        assert self.task.dag
                    execution_date = timezone.make_aware(execution_date, self.task.dag.timezone)
                else:
                    execution_date = timezone.make_aware(execution_date)
                execution_date = timezone.convert_to_utc(execution_date)
            with create_session() as session:
                run_id = (
                    session.query(DagRun.run_id)
                    .filter_by(dag_id=self.dag_id, execution_date=execution_date)
                    .scalar()
                )
                if run_id is None:
                    raise DagRunNotFound(
                        f"DagRun for {self.dag_id!r} with date {execution_date} not found"
                    ) from None
        self.run_id = run_id
        self.try_number = 0
        self.max_tries = self.task.retries
        self.unixname = getuser()
        if state:
            self.state = state
        self.hostname = ""
        
        
        self.raw = False
        
        self.test_mode = False
    @staticmethod
    def insert_mapping(run_id: str, task: Operator, map_index: int) -> dict[str, Any]: