### 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)

synchronize_session=False,
            )
        for ti in executable_tis:
            make_transient(ti)
        return executable_tis
    def _enqueue_task_instances_with_queued_state(self, task_instances: list[TI], session: Session) -> None:
        """
        Enqueue task_instances which should have been set to queued with the executor.
        :param task_instances: TaskInstances to enqueue
        :param session: The session object
        """
        
        for ti in task_instances:
            if ti.dag_run.state in State.finished:
                ti.set_state(State.NONE, session=session)
                continue
            command = ti.command_as_list(
                local=True,
                pickle_id=ti.dag_model.pickle_id,
            )
            priority = ti.priority_weight
            queue = ti.queue
            self.log.info("Sending %s to executor with priority %s and queue %s", ti.key, priority, queue)
            self.job.executor.queue_command(
                ti,
                command,
                priority=priority,
                queue=queue,
            )
    def _critical_section_enqueue_task_instances(self, session: Session) -> int: