### Apache Airflow version

Other Airflow 2 version (please specify below)

### What happened

Airflow version: **2.5.3**

Related to this comment from @vchiapaikeo: https://github.com/apache/airflow/pull/33172#issuecomment-1677501450

A couple of mins after midnight UTC - when 100s of DAGs are kicked off - we noticed our triggerer replicas failing liveness probe checks and restarting systematically.

Further profiling led to the discovery that the triggerer’s sync loop hangs for several minutes when there are 1000s of triggers running simultaneously, specifically while [bulk fetching triggers](https://github.com/apache/airflow/blob/v2-5-test/airflow/jobs/triggerer_job.py#L398), which causes the triggerer to miss heartbeats and eventually get restarted by k8s.

With profiling still enabled, we observed that while the trigger is hanging and we profile the execution, we get this stack trace:

```
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           [506/45463]
        1    0.000    0.000   29.928   29.928 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/query.py:2757(all)               
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1468(all)      
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:395(_allrows)  
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1388(_fetchall_impl)       
        1    0.000    0.000   29.923   29.923 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:1808(_fetchall_impl)                                                                                                                                                                                                                                                                                                                                                                                
        2    0.000    0.000   29.922   14.961 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/orm/loading.py:135(chunks)
        1    0.000    0.000   29.921   29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:390(_raw_all_rows)
        1    0.001    0.001   29.921   29.921 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/engine/result.py:393(<listcomp>)                                                                                                                                                                                                                                                                                                                                                                                                                        
      125    0.000    0.000   29.919    0.239 /home/airflow/.local/lib/python3.10/site-packages/sqlalchemy/sql/type_api.py:1711(process) 
      125    0.002    0.000   29.915    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:146(process_result_value)  
      125    0.001    0.000   29.909    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/utils/sqlalchemy.py:122(db_supports_json)    
      125    0.001    0.000   29.908    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:562(get)                  
      125    0.000    0.000   29.907    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:732(_get_environment_variables)
      125    0.002    0.000   29.907    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:478(_get_env_var_option)
      125    0.002    0.000   29.902    0.239 /home/airflow/.local/lib/python3.10/site-packages/airflow/configuration.py:103(run_command)                
      125    0.001    0.000   29.786    0.238 /usr/local/lib/python3.10/subprocess.py:1110(communicate)                                                       
      125    0.006    0.000   29.785    0.238 /usr/local/lib/python3.10/subprocess.py:1952(_communicate)                                             
      250    0.003    0.000   29.762    0.119 /usr/local/lib/python3.10/selectors.py:403(select)                                         
      250   29.758    0.119   29.758    0.119 {method 'poll' of 'select.poll' objects}                                                                  
      125    0.002    0.000    0.100    0.001 /usr/local/lib/python3.10/subprocess.py:758(__init__)                                                                  
      125    0.004    0.000    0.094    0.001 /usr/local/lib/python3.10/subprocess.py:1687(_execute_child)
```

Which indicates that airflow is running a subprocess for each fetched row and that takes the vast majority of the execution time.

We found that during the unmarshaling of the resulting rows into the Trigger model, the [kwargs column](https://github.com/apache/airflow/blob/v2-5-test/airflow/models/trigger.py#L57) (ExtendedJSON) runs [process_returned_value](https://github.com/apache/airflow/blob/v2-5-test/airflow/utils/sqlalchemy.py#L146), on each row, and reads the `SQL_ALCHEMY_CONN` configuration to determine whether the engine supports json or not and parse kwargs accordingly. However, in our case we define `SQL_ALCHEMY_CONN_CMD` as opposed to `SQL_ALCHEMY_CONN`, which causes the sync loop to spawn a new subprocess for every row ([here](https://github.com/apache/airflow/blob/v2-5-test/airflow/configuration.py#L485-L488)).

We workaround it by using `SQL_ALCHEMY_CONN` instead of `SQL_ALCHEMY_CONN_CMD`, as it involves reading an environment variable instead of spawning a new subprocess.

### What you think should happen instead

The triggerer model caches caches either the `SQL_ALCHEMY_CONN` or the [db_supports_json](https://github.com/apache/airflow/blob/v2-5-stable/airflow/utils/sqlalchemy.py#L122) property.

### How to reproduce

Simultaneously kick off 100s of DAGs with at least a few deferrable operators each and use `SQL_ALCHEMY_CONN_CMD` instead of `SQL_ALCHEMY_CONN`

### Operating System

Debian GNU/Linux 11 (bullseye)

### Versions of Apache Airflow Providers

apache-airflow-providers-airbyte==3.2.0
apache-airflow-providers-alibaba==2.2.0
apache-airflow-providers-amazon==7.3.0
apache-airflow-providers-apache-beam==4.3.0
apache-airflow-providers-apache-cassandra==3.1.1
apache-airflow-providers-apache-drill==2.3.1
apache-airflow-providers-apache-druid==3.3.1
apache-airflow-providers-apache-hdfs==3.2.0
apache-airflow-providers-apache-hive==5.1.3
apache-airflow-providers-apache-kylin==3.1.0
apache-airflow-providers-apache-livy==3.3.0
apache-airflow-providers-apache-pig==4.0.0
apache-airflow-providers-apache-pinot==4.0.1
apache-airflow-providers-apache-spark==4.0.0
apache-airflow-providers-apache-sqoop==3.1.1
apache-airflow-providers-arangodb==2.1.1
apache-airflow-providers-asana==2.1.0
apache-airflow-providers-atlassian-jira==2.0.1
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cloudant==3.1.0
apache-airflow-providers-cncf-kubernetes==5.2.2
apache-airflow-providers-common-sql==1.3.4
apache-airflow-providers-databricks==4.0.0
apache-airflow-providers-datadog==3.1.0
apache-airflow-providers-dbt-cloud==3.1.0
apache-airflow-providers-dingding==3.1.0
apache-airflow-providers-discord==3.1.0
apache-airflow-providers-docker==3.5.1
apache-airflow-providers-elasticsearch==4.4.0
apache-airflow-providers-exasol==4.1.3
apache-airflow-providers-facebook==3.1.0
apache-airflow-providers-ftp==3.3.1
apache-airflow-providers-github==2.2.1
apache-airflow-providers-google==8.11.0
apache-airflow-providers-grpc==3.1.0
apache-airflow-providers-hashicorp==3.3.0
apache-airflow-providers-http==4.2.0
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-influxdb==2.1.0
apache-airflow-providers-jdbc==3.3.0
apache-airflow-providers-jenkins==3.2.0
apache-airflow-providers-microsoft-azure==5.2.1
apache-airflow-providers-microsoft-mssql==3.3.2
apache-airflow-providers-microsoft-psrp==2.2.0
apache-airflow-providers-microsoft-winrm==3.1.1
apache-airflow-providers-mongo==3.1.1
apache-airflow-providers-mysql==4.0.2
apache-airflow-providers-neo4j==3.2.1
apache-airflow-providers-odbc==3.2.1
apache-airflow-providers-openfaas==3.1.0
apache-airflow-providers-opsgenie==5.0.0
apache-airflow-providers-oracle==3.6.0
apache-airflow-providers-pagerduty==3.1.0
apache-airflow-providers-papermill==3.1.1
apache-airflow-providers-plexus==3.1.0
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-presto==4.2.2
apache-airflow-providers-qubole==3.3.1
apache-airflow-providers-redis==3.1.0
apache-airflow-providers-salesforce==5.3.0
apache-airflow-providers-samba==4.1.0
apache-airflow-providers-segment==3.1.0
apache-airflow-providers-sendgrid==3.1.0
apache-airflow-providers-sftp==4.2.4
apache-airflow-providers-singularity==3.1.0
apache-airflow-providers-slack==7.2.0
apache-airflow-providers-snowflake==4.0.4
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.5.0
apache-airflow-providers-tableau==4.1.0
apache-airflow-providers-tabular==1.1.0
apache-airflow-providers-telegram==4.0.0
apache-airflow-providers-trino==4.3.2
apache-airflow-providers-vertica==3.3.1
apache-airflow-providers-yandex==3.3.0
apache-airflow-providers-zendesk==4.2.0


### Deployment

Other 3rd-party Helm chart

### Deployment details

Chart based on the official helm chart. Airflow running on Google Kubernetes Engine (GKE) using `KubernetesExecutor`.

### Anything else

_No response_

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

columns: tuple[ColumnElement, ...],
    collection: Iterable[Any] | Select,
    *,
    session: Session | None = None,
) -> ColumnOperators:
    """
    Generate a tuple-in-collection operator to use in ``.where()``.
    For most SQL backends, this generates a simple ``([col, ...]) IN [condition]``
    clause. This however does not work with MSSQL, where we need to expand to
    ``(c1 = v1a AND c2 = v2a ...) OR (c1 = v1b AND c2 = v2b ...) ...`` manually.
    :meta private:
    """
    if settings.engine.dialect.name != "mssql":
        return tuple_(*columns).in_(collection)
    if not isinstance(collection, Select):
        rows = collection
    elif session is None:
        raise TypeError("session is required when passing in a subquery")
    else:
        rows = session.execute(collection)
    clauses = [and_(*(c == v for c, v in zip(columns, values))) for values in rows]
    if not clauses:
        return false()
    return or_(*clauses)
@overload
def tuple_not_in_condition(