### 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)

from airflow.utils.trigger_rule import TriggerRule
from airflow.utils.weight_rule import WeightRule
TaskStateChangeCallback = Callable[[Context], None]
if TYPE_CHECKING:
    import jinja2  
    from sqlalchemy.orm import Session
    from airflow.models.baseoperator import BaseOperator, BaseOperatorLink
    from airflow.models.dag import DAG
    from airflow.models.mappedoperator import MappedOperator
    from airflow.models.operator import Operator
    from airflow.models.taskinstance import TaskInstance
DEFAULT_OWNER: str = conf.get_mandatory_value("operators", "default_owner")
DEFAULT_POOL_SLOTS: int = 1
DEFAULT_PRIORITY_WEIGHT: int = 1
DEFAULT_QUEUE: str = conf.get_mandatory_value("operators", "default_queue")
DEFAULT_IGNORE_FIRST_DEPENDS_ON_PAST: bool = conf.getboolean(
    "scheduler", "ignore_first_depends_on_past_by_default"
)
DEFAULT_RETRIES: int = conf.getint("core", "default_task_retries", fallback=0)
DEFAULT_RETRY_DELAY: datetime.timedelta = datetime.timedelta(
    seconds=conf.getint("core", "default_task_retry_delay", fallback=300)
)
DEFAULT_WEIGHT_RULE: WeightRule = WeightRule(
    conf.get("core", "default_task_weight_rule", fallback=WeightRule.DOWNSTREAM)
)
DEFAULT_TRIGGER_RULE: TriggerRule = TriggerRule.ALL_SUCCESS
DEFAULT_TASK_EXECUTION_TIMEOUT: datetime.timedelta | None = conf.gettimedelta(
    "core", "default_task_execution_timeout"
)
class NotMapped(Exception):