### 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)

schedule_delay,
                    tags={"dag_id": dag.dag_id},
                )
        for dag_run in dag_runs:
            dag = dag_run.dag = self.dagbag.get_dag(dag_run.dag_id, session=session)
            if not dag:
                self.log.error("DAG '%s' not found in serialized_dag table", dag_run.dag_id)
                continue
            active_runs = active_runs_of_dags[dag_run.dag_id]
            if dag.max_active_runs and active_runs >= dag.max_active_runs:
                self.log.debug(
                    "DAG %s already has %d active runs, not moving any more runs to RUNNING state %s",
                    dag.dag_id,
                    active_runs,
                    dag_run.execution_date,
                )
            else:
                active_runs_of_dags[dag_run.dag_id] += 1
                _update_state(dag, dag_run)
                dag_run.notify_dagrun_state_changed()
    @retry_db_transaction
    def _schedule_all_dag_runs(self, guard, dag_runs, session):
        """Makes scheduling decisions for all `dag_runs`"""
        callback_tuples = []
        for dag_run in dag_runs:
            callback_to_run = self._schedule_dag_run(dag_run, session)
            callback_tuples.append((dag_run, callback_to_run))
        guard.commit()
        return callback_tuples
    def _schedule_dag_run(