Getting historical features from a feature view with multiple entity keys took too long

I am encountering a performance issue when retrieving historical features from a multiple entity keys feature view in Feast, using a PostgreSQL offline store.
I have two tables in PostgreSQL - a raw events table with a primary key and an aggregated data table with a composite key. The events table is significantly larger, containing approximately 10 times the number of rows as the aggregated table.

## Expected Behavior
Retrieval of historical features from the aggregated data feature view, should be comparable in performance to the retrieval from the raw events feature view.

## Current Behavior
Retrieving historical features from the feature view based on the raw events table (larger dataset) completes within a minute. In contrast, the same operation on the aggregated data feature view with multiple entity keys does not complete (runs indefinitely).

## Steps to reproduce
Here's a simplified code snippet to illustrate the setup and the issue:
```python
from feast import BigQuerySource, Entity, FeatureView, Field
from feast.types import Float32, Int64, String
from feast.infra.offline_stores.contrib.postgres_offline_store.postgres_source import (
PostgreSQLSource,
)

event = Entity(name="event", join_keys=["event_id"])
user = Entity(name="user", join_keys=["user_id"])
item = Entity(name="item", join_keys=["item_id"])

events_source = PostgreSQLSource(
name="events",
query="SELECT * FROM events",
timestamp_field="event_timestamp"
)

user_stats_source = PostgreSQLSource(
name="user_stats",
query="SELECT * FROM user_stats",
timestamp_field="event_timestamp"
)

events_fv = FeatureView(
name="event_fv",
entities=[event],
schema=[
Field(name="user_id", dtype=String),
Field(name="item_id", dtype=String)
Field(name="val", dtype=Int64)
],
source=events_source
)

user_stats_fv = FeatureView(
name="user_activity",
entities=[user, item],
schema=[
Field(name="val", dtype=Int64)
],
source=user_stats_source
)
```
Retrieve offline features for training
```python
from feast import FeatureStore

store = FeatureStore(repo_path=".")

# Get the latest feature values for unique entities
entity_sql = f"""
SELECT
user_id,
item_id,
CURRENT_TIMESTAMP() as event_timestamp
FROM {store.get_data_source("user_stats").get_table_query_string()}
WHERE event_timestamp BETWEEN '2023-01-01' and '2023-12-31'
"""
data = store.get_historical_features(
entity_df=entity_sql,
features=store.get_feature_service("model_v2"),
).to_df()
```

### Specifications

- Version: 0.34.1
- Platform: Linux
- Subsystem: CentOS Linux release 7.9.2009 (Core)

## Possible Solution
A workaround that improved performance was to combine multiple keys into a single key by concatenating strings. This approach made historical feature retrieval from the aggregated table faster than from the raw events table. However, this feels more like a workaround than a solution, and I am seeking advice or a fix for handling multiple entity keys efficiently without resorting to key concatenation.