### 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 self.next_method:
                timeout_seconds = (
                    task_to_execute.execution_timeout - (timezone.utcnow() - self.start_date)
                ).total_seconds()
            else:
                timeout_seconds = task_to_execute.execution_timeout.total_seconds()
            try:
                
                if timeout_seconds <= 0:
                    raise AirflowTaskTimeout()
                
                with timeout(timeout_seconds):
                    result = execute_callable(context=context)
            except AirflowTaskTimeout:
                task_to_execute.on_kill()
                raise
        else:
            result = execute_callable(context=context)
        with create_session() as session:
            if task_to_execute.do_xcom_push:
                xcom_value = result
            else:
                xcom_value = None
            if xcom_value is not None:  
                self.xcom_push(key=XCOM_RETURN_KEY, value=xcom_value, session=session)
            self._record_task_map_for_downstreams(task_orig, xcom_value, session=session)
        return result
    @provide_session
    def _defer_task(self, session: Session, defer: TaskDeferred) -> None: