If you have a DAG with `max_active_runs=1` and it is running some tasks, and then you manually trigger another run of this DAG, both runs end up not scheduling anything and you see this message over and over in the logs:

```
[2020-10-16 14:00:34,118]  2437572 {{airflow.jobs.scheduler_job.SchedulerJob scheduler_job.py:1657}} INFO - DAG scenario1_case2_01_1 already has 1 active runs, not queuing any more tasks
```

A longer term fix for this would be to introduce a "queued" state for DagRuns, and then when manually creating dag runs set it to queued, and only have the scheduler set DagRuns to running.

That may or may not be less work than fixing the current mechanism.
airflow/jobs/scheduler_job.py
"""
        This function is where the main scheduling decisions take places. It:
        - Creates any necessary DAG runs by examining the next_dagrun_create_after column of DagModel
          Since creating Dag Runs is a relatively time consuming process, we select only 10 dags by default
          (configurable via ``scheduler.max_dagruns_to_create_per_loop`` setting) - putting this higher will
          mean one scheduler could spend a chunk of time creating dag runs, and not ever get around to
          scheduling tasks.
        - Finds the "next n oldest" running DAG Runs to examine for scheduling (n=20 by default, configurable
          via ``scheduler.max_dagruns_per_loop_to_schedule`` config setting) and tries to progress state (TIs
          to SCHEDULED, or DagRuns to SUCCESS/FAILURE etc)
          By "next oldest", we mean hasn't been examined/scheduled in the most time.
          The reason we don't select all dagruns at once because the rows are selected with row locks, meaning
          that only one scheduler can "process them", even it it is waiting behind other dags. Increasing this
          limit will allow more throughput for smaller DAGs but will likely slow down throughput for larger
          (>500 tasks.) DAGs
        - Then, via a Critical Section (locking the rows of the Pool model) we queue tasks, and then send them
          to the executor.
          See docs of _critical_section_execute_task_instances for more.
        :return: Number of TIs enqueued in this iteration
        :rtype: int
        """
        
        with prohibit_commit(session) as guard:
            if settings.USE_JOB_SCHEDULE:
                query = DagModel.dags_needing_dagruns(session)
                self._create_dag_runs(query.all(), session)
                
                guard.commit()
                
            dag_runs = DagRun.next_dagruns_to_examine(session)