### 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)

"""
        logical_date = timezone.coerce_datetime(execution_date)
        if data_interval and not isinstance(data_interval, DataInterval):
            data_interval = DataInterval(*map(timezone.coerce_datetime, data_interval))
        if data_interval is None and logical_date is not None:
            warnings.warn(
                "Calling `DAG.create_dagrun()` without an explicit data interval is deprecated",
                RemovedInAirflow3Warning,
                stacklevel=3,
            )
            if run_type == DagRunType.MANUAL:
                data_interval = self.timetable.infer_manual_data_interval(run_after=logical_date)
            else:
                data_interval = self.infer_automated_data_interval(logical_date)
        if run_type is None or isinstance(run_type, DagRunType):
            pass
        elif isinstance(run_type, str):  
            run_type = DagRunType(run_type)
        else:
            raise ValueError(f"`run_type` should be a DagRunType, not {type(run_type)}")
        if run_id:  
            if not isinstance(run_id, str):
                raise ValueError(f"`run_id` should be a str, not {type(run_id)}")
            inferred_run_type = DagRunType.from_run_id(run_id)
            if run_type is None:
                
                run_type = inferred_run_type
            elif run_type == DagRunType.MANUAL and inferred_run_type != DagRunType.MANUAL:
                
                raise ValueError(