**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
try:
            
            with redirect_stdout(StreamLogWriter(log, logging.INFO)), redirect_stderr(
                StreamLogWriter(log, logging.WARN)
            ), Stats.timer() as timer:
                
                settings.configure_orm()
                
                
                
                threading.current_thread().name = thread_name
                log.info("Started process (PID=%s) to work on %s", os.getpid(), file_path)
                dag_file_processor = DagFileProcessor(dag_ids=dag_ids, log=log)
                result: Tuple[int, int] = dag_file_processor.process_file(
                    file_path=file_path,
                    pickle_dags=pickle_dags,
                    callback_requests=callback_requests,
                )
                result_channel.send(result)
            log.info("Processing %s took %.3f seconds", file_path, timer.duration)
        except Exception:  
            
            log.exception("Got an exception! Propagating...")
            raise
        finally:
            
            
            settings.dispose_orm()
            result_channel.close()
    def start(self) -> None: