### 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)

"""
    SchedulerJobRunner runs for a specific time interval and schedules jobs that are ready to run.
    It figures out the latest runs for each task and sees if the dependencies
    for the next schedules are met.
    If so, it creates appropriate TaskInstances and sends run commands to the
    executor. It does this for each task in each DAG and repeats.
    :param subdir: directory containing Python files with Airflow DAG
        definitions, or a specific path to a file
    :param num_runs: The number of times to run the scheduling loop. If you
        have a large number of DAG files this could complete before each file
        has been parsed. -1 for unlimited times.
    :param num_times_parse_dags: The number of times to try to parse each DAG file.
        -1 for unlimited times.
    :param scheduler_idle_sleep_time: The number of seconds to wait between
        polls of running processors
    :param do_pickle: once a DAG object is obtained by executing the Python
        file, whether to serialize the DAG object to the DB
    :param log: override the default Logger
    """
    job_type = "SchedulerJob"
    heartrate: int = conf.getint("scheduler", "SCHEDULER_HEARTBEAT_SEC")
    def __init__(