Task end_date prevents downstream tasks from running

### Apache Airflow version

Other Airflow 2 version (please specify below)

### If "Other Airflow 2 version" selected, which one?

2.7.1

### What happened?

I have a task with multiple upstream dependencies, one of which is a deprecated dataset. To retire this dataset, I set its task's `end_date` to the last day that the data was available, and I updated the code for the downstream task to stop looking for that data. Now, the downstream task is stuck in "No status" and must be manually started from the command line.

The only `trigger_rule`s that work around this issue are ones that have the wrong semantics for my task (e.g. `always`). I also want to make sure we can still backfill the deprecated dataset if needed, so I want to keep the DAG structure and dependencies backward compatible.

### What you think should happen instead?

In descending order of personal preference:

1. The downstream task should only be prevented from running if _all_ of its upstream dependencies are outside of their `start_date`/`end_date` window, not just one.
2. A task outside of its date window should behave like a "skipped" task, propagating down to children but allowing downstream behavior to be controlled via `trigger_rule`s.
3. If the scheduling behavior cannot be changed, perhaps a workaround would be to expose the `logical_date` at the DAG level via `AirflowParsingContext`, so that dependencies could be conditionally changed:
```
if deprecated_task.start_date <= get_parsing_context().logical_date <= deprecated_task.end_date:
deprecated_task >> downstream_task
```

### How to reproduce

```
#!/usr/bin/env python3
import datetime
import logging

from airflow.decorators import dag
from airflow.models.baseoperator import cross_downstream
from airflow.operators.empty import EmptyOperator
from airflow.utils.task_group import TaskGroup

logger = logging.getLogger(__name__)

@dag(
schedule='@daily',
start_date=datetime.datetime(2024, 10, 21),
)
def test_task_end_date():
with TaskGroup(group_id='single_dep'):
(
EmptyOperator(task_id='upstream')
>> EmptyOperator(task_id='expired', end_date=datetime.datetime(2024, 10, 24))
>> EmptyOperator(task_id='downstream')
)

with TaskGroup(group_id='multiple_deps'):
[
EmptyOperator(task_id='upstream_1'),
EmptyOperator(task_id='upstream_2'),
EmptyOperator(task_id='expired', end_date=datetime.datetime(2024, 10, 24)),
] >> EmptyOperator(task_id='downstream')

with TaskGroup(group_id='trigger_rules'):
cross_downstream([
EmptyOperator(task_id='upstream_1'),
EmptyOperator(task_id='upstream_2'),
EmptyOperator(task_id='expired', end_date=datetime.datetime(2024, 10, 24)),
], [
EmptyOperator(task_id='all_success'),
EmptyOperator(task_id='all_done', trigger_rule='all_done'),
EmptyOperator(task_id='one_success', trigger_rule='one_success'),
EmptyOperator(task_id='none_failed', trigger_rule='none_failed'),
EmptyOperator(task_id='none_failed_min_one_success', trigger_rule='none_failed_min_one_success'),
EmptyOperator(task_id='none_skipped', trigger_rule='none_skipped'),
EmptyOperator(task_id='always', trigger_rule='always'),
])

dag = test_task_end_date()

if __name__ == '__main__':
dag.cli()
```


### Operating System

CentOS Stream 8

### Versions of Apache Airflow Providers

N/A

### Deployment

Other

### Deployment details

Self-hosted/standalone

### 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)

Start with the supplied Python DAG reproduction, focusing on task end_date handling, downstream trigger_rule evaluation, and the AirflowParsingContext entry point mentioned in the report. Compare the single- and multiple-dependency cases and determine which documented behavior should apply. Done means the chosen behavior works with appropriate trigger rules while preserving the reported backfill requirement.

Written by the indexing model from the issue text.