**Apache Airflow version**: 2.0.0


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**:
- **OS** (e.g. from /etc/os-release):
- **Kernel** (e.g. `uname -a`):
- **Install tools**:
- **Others**:

**What happened**:

I have the following DAG:

```py
from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators.bash_operator import BashOperator

with DAG(
    dag_id="sla_trigger",
    schedule_interval="* * * * *",
    start_date=datetime(2020, 2, 3),
) as dag:
    BashOperator(
        task_id="bash_task",
        bash_command="sleep 30",
        sla=timedelta(seconds=2),
    )

```

And in my understanding this dag should result in SLA miss every time it is triggered (every minute). However, after few minutes of running I don't see any SLA misses...

**What you expected to happen**:

I expect to see SLA if task takes longer than expected.

**How to reproduce it**:

Use the dag from above.

**Anything else we need to know**:

N/A

airflow/jobs/scheduler_job.py
task_instance.pool_slots,
                        open_slots,
                        pool,
                    )
                    num_starving_tasks += 1
                    num_starving_tasks_total += 1
                    
                    continue
                executable_tis.append(task_instance)
                open_slots -= task_instance.pool_slots
                dag_concurrency_map[dag_id] += 1
                task_concurrency_map[(task_instance.dag_id, task_instance.task_id)] += 1
            Stats.gauge(f'pool.starving_tasks.{pool_name}', num_starving_tasks)
        Stats.gauge('scheduler.tasks.starving', num_starving_tasks_total)
        Stats.gauge('scheduler.tasks.running', num_tasks_in_executor)
        Stats.gauge('scheduler.tasks.executable', len(executable_tis))
        task_instance_str = "\n\t".join([repr(x) for x in executable_tis])
        self.log.info("Setting the following tasks to queued state:\n\t%s", task_instance_str)
        
        filter_for_tis = TI.filter_for_tis(executable_tis)
        session.query(TI).filter(filter_for_tis).update(
            
            
            {TI.state: State.QUEUED, TI.queued_dttm: timezone.utcnow(), TI.queued_by_job_id: self.id},
            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]) -> None: