**Apache Airflow version**: 2.0.0
**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
**Environment**:

- **OS** (e.g. from /etc/os-release): Debian GNU/Linux 10 (buster)
- **Kernel** (e.g. `uname -a`): Linux 6ae65b86e112 5.4.0-52-generic #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020 x86_64 GNU/Linux
- **Others**: Python 3.8

**What happened**:

After migrating one of our development Airflow instances from 1.10.14 to 2.0.0, the scheduler started to refuse to schedule tasks for a DAG that did not actually exceed its `max_active_runs`.

When it did this the following error would be logged:

```
DAG <dag_name> already has 577 active runs, not queuing any tasks for run 2020-12-17 08:05:00+00:00
```

A bit of digging revealed that this DAG had task instances associated with it that are in the `removed` state. As soon as I forced the task instances that are in the `removed` state into the `failed` state, the tasks would be scheduled.

I believe the root cause of the issue is that [this filter](https://github.com/apache/airflow/blob/master/airflow/jobs/scheduler_job.py#L1506) does not filter out tasks that are in the `removed` state.

**What you expected to happen**:

I expected the task instances in the DAG to be scheduled, because the DAG did not actually exceed the number of `max_active_runs`.

**How to reproduce it**:

I think the best approach to reproduce it is as follows:
1. Create a DAG and set `max_active_runs` to 1.
2. Ensure the DAG has ran successfully a number of times, such that it has some history associated with it.
3. Set one historical task instance to the `removed` state (either by directly updating it in the DB, or deleting a task from a DAG before it has been able to execute).

**Anything else we need to know**:

The Airflow instance that I ran into this issue with contains about 3 years of task history, which means that we actually had quite a few task instances that are in the `removed` state, but there is no easy way to delete those from the Web UI.

A work around is to set the tasks to `failed`, which will allow the scheduler to proceed.
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)