### Apache Airflow version

2.5.1

### What happened

I'd expect the `scheduler.tasks.running` metric to represent the number of running tasks, but it is always zero. It appears that #10956 broke this when it removed [the line that increments `num_tasks_in_executor`](https://github.com/apache/airflow/pull/10956/files#diff-bde85feb359b12bdd358aed4106ef4fccbd8fa9915e16b9abb7502912a1c1ab3L1363). Right now that variable is set to 0, never incremented, and the emitted as a gauge.

### What you think should happen instead

`scheduler.tasks.running` should either represent the number of tasks running or be removed altogether.

### How to reproduce

_No response_

### Operating System

Ubuntu 18.04

### Versions of Apache Airflow Providers

_No response_

### Deployment

Official Apache Airflow Helm Chart

### Deployment details

_No response_

### Anything else

_No response_

### Are you willing to submit PR?

- [X] 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)

self._send_sla_callbacks_to_processor(dag)
        if callback:
            self.job.executor.send_callback(callback)
        else:
            self.log.debug("callback is empty")
    def _send_sla_callbacks_to_processor(self, dag: DAG) -> None:
        """Sends SLA Callbacks to DagFileProcessor if tasks have SLAs set and check_slas=True."""
        if not settings.CHECK_SLAS:
            return
        if not any(isinstance(task.sla, timedelta) for task in dag.tasks):
            self.log.debug("Skipping SLA check for %s because no tasks in DAG have SLAs", dag)
            return
        if not dag.timetable.periodic:
            self.log.debug("Skipping SLA check for %s because DAG is not scheduled", dag)
            return
        dag_model = DagModel.get_dagmodel(dag.dag_id)
        if not dag_model:
            self.log.error("Couldn't find DAG %s in database!", dag.dag_id)
            return
        request = SlaCallbackRequest(
            full_filepath=dag.fileloc,
            dag_id=dag.dag_id,
            processor_subdir=dag_model.processor_subdir,
        )
        self.job.executor.send_callback(request)
    @provide_session
    def _emit_pool_metrics(self, session: Session = NEW_SESSION) -> None: