### 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)

"""Fetch rendered template fields from DB"""
        from airflow.models.renderedtifields import RenderedTaskInstanceFields
        rendered_k8s_spec = RenderedTaskInstanceFields.get_k8s_pod_yaml(self, session=session)
        if not rendered_k8s_spec:
            try:
                rendered_k8s_spec = self.render_k8s_pod_yaml()
            except (TemplateAssertionError, UndefinedError) as e:
                raise AirflowException(f"Unable to render a k8s spec for this taskinstance: {e}") from e
        return rendered_k8s_spec
    def overwrite_params_with_dag_run_conf(self, params, dag_run):
        """Overwrite Task Params with DagRun.conf"""
        if dag_run and dag_run.conf:
            self.log.debug("Updating task params (%s) with DagRun.conf (%s)", params, dag_run.conf)
            params.update(dag_run.conf)
    def render_templates(self, context: Context | None = None) -> Operator:
        """Render templates in the operator fields.
        If the task was originally mapped, this may replace ``self.task`` with
        the unmapped, fully rendered BaseOperator. The original ``self.task``
        before replacement is returned.
        """
        if not context:
            context = self.get_template_context()
        original_task = self.task
        
        
        
        
        original_task.render_template_fields(context)
        return original_task
    def render_k8s_pod_yaml(self) -> dict | None: