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**Apache Airflow version**: 2.0.0 but applicable to master

**Environment**: Running on ECS but not relevant to question


**What happened**: I am not seeing the metric dagrun.schedule_delay.<dag_id> being reported.  A search in the codebase seems to reveal that it no longer exists.   It was originally added in https://github.com/apache/airflow/pull/5050.

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<!-- What do you think went wrong? -->
I suspect either:
1.  This metric was intentionally removed, in which case the docs should be updated to remove it.
2. It was unintentionally removed during a refactor, in which case we should add it back.
3. I am bad at searching through code, and someone could hopefully point me to where it is reported from now. 

**How to reproduce it**:
https://github.com/apache/airflow/search?q=schedule_delay


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airflow/jobs/scheduler_job.py
command,
                priority=priority,
                queue=queue,
            )
    def _critical_section_execute_task_instances(self, session: Session) -> int:
        """
        Attempts to execute TaskInstances that should be executed by the scheduler.
        There are three steps:
        1. Pick TIs by priority with the constraint that they are in the expected states
        and that we do exceed max_active_runs or pool limits.
        2. Change the state for the TIs above atomically.
        3. Enqueue the TIs in the executor.
        HA note: This function is a "critical section" meaning that only a single executor process can execute
        this function at the same time. This is achieved by doing ``SELECT ... from pool FOR UPDATE``. For DBs
        that support NOWAIT, a "blocked" scheduler will skip this and continue on with other tasks (creating
        new DAG runs, progressing TIs from None to SCHEDULED etc.); DBs that don't support this (such as
        MariaDB or MySQL 5.x) the other schedulers will wait for the lock before continuing.
        :param session:
        :type session: sqlalchemy.orm.Session
        :return: Number of task instance with state changed.
        """
        if self.max_tis_per_query == 0:
            max_tis = self.executor.slots_available
        else:
            max_tis = min(self.max_tis_per_query, self.executor.slots_available)
        queued_tis = self._executable_task_instances_to_queued(max_tis, session=session)
        self._enqueue_task_instances_with_queued_state(queued_tis)
        return len(queued_tis)
    @provide_session
    def _change_state_for_tasks_failed_to_execute(self, session: Session = None):