create a new index for span_status, dag_version_id in task_instance table

### Description

When the TI table is huge(~ 10M records), , `_end_spans_of_externally_ended_ops` method([Link](https://github.com/apache/airflow/blob/912ccace3532e3c05a46f6a1cf9457c0de41db6b/airflow-core/src/airflow/jobs/scheduler_job_runner.py#L1085C9-L1085C43)) takes very long to finish(in our case, about 75 seconds).

This particular sqlalchemy query was taking too long:
```
tis_should_end: list[TaskInstance] = session.scalars(
select(TaskInstance).where(TaskInstance.span_status == SpanStatus.SHOULD_END)
).all()
```

After adding the following index, performance improved significantly

```
create index idx_span_status on task_instance (id, span_status);
```

Along with above, creating an index on dag_version_id(`create index idx_dag_version_id on task_instance (dag_version_id);`) also helped to improve DAG Processor performance.
### Use case/motivation

Improve scheduler and Dag Processor performance when TI table has lot of records

### Related issues

_No response_

### Are you willing to submit a 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 `_end_spans_of_externally_ended_ops` entry point in `airflow-core/src/airflow/jobs/scheduler_job_runner.py` and the shown SQLAlchemy query on the `task_instance` table. Review the project’s schema-change entry points, then add and verify indexes for `span_status` and `dag_version_id`; done means both scheduler and DAG Processor queries have the intended indexes.

Written by the indexing model from the issue text.