### What do you see as an issue?

The [/dags/{dag_id}/details](https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/post_set_task_instances_state) endpoint of the REST API does not return all of the keys that are listed in the documentation. If I run `curl -X GET localhost:8080/api/v1/dags/{my_dag}/details`, then compare the results with the results in the documentation, you can see the following missing keys:

```python
>>> for key in docs.keys():
...     if not key in actual.keys():
...             print(key)
...
root_dag_id
last_parsed_time
last_pickled
last_expired
scheduler_lock
timetable_description
has_task_concurrency_limits
has_import_errors
next_dagrun
next_dagrun_data_interval_start
next_dagrun_data_interval_end
next_dagrun_create_after
template_search_path
```

### Solving the problem

Either remove these keys from the documentation or fix the API endpoint

### 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)

"""
        Return (and lock) a list of Dag objects that are due to create a new DagRun.
        This will return a resultset of rows that is row-level-locked with a "SELECT ... FOR UPDATE" query,
        you should ensure that any scheduling decisions are made in a single transaction -- as soon as the
        transaction is committed it will be unlocked.
        """
        from airflow.models.dataset import DagScheduleDatasetReference, DatasetDagRunQueue as DDRQ
        
        dataset_triggered_dag_info = {
            x.dag_id: (x.first_queued_time, x.last_queued_time)
            for x in session.execute(
                select(
                    DagScheduleDatasetReference.dag_id,
                    func.max(DDRQ.created_at).label("last_queued_time"),
                    func.min(DDRQ.created_at).label("first_queued_time"),
                )
                .join(DagScheduleDatasetReference.queue_records, isouter=True)
                .group_by(DagScheduleDatasetReference.dag_id)
                .having(func.count() == func.sum(case((DDRQ.target_dag_id.is_not(None), 1), else_=0)))
            )
        }
        dataset_triggered_dag_ids = set(dataset_triggered_dag_info)
        if dataset_triggered_dag_ids:
            exclusion_list = set(
                session.scalars(
                    select(DagModel.dag_id)
                    .join(DagRun.dag_model)
                    .where(DagRun.state.in_((DagRunState.QUEUED, DagRunState.RUNNING)))