fit_transform tries to access embedding_model if representation_model is not None

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### Desribe the bug

I was using BERTopic on a cluster of queries with my own embeddings (computed on a model that is hard to pass as a parameter) and it was working as expected.
After trying to use `representation_model = KeyBERTInspired()` and adding ` representation_model=representation_model` to BERTopic as a parameter. I got this error :

```
AttributeError Traceback (most recent call last)
[1] representation_model = KeyBERTInspired()
[2] topic_model = BERTopic(
[3] calculate_probabilities=True,
[4] min_topic_size=1
[5] embedding_model=None,
[6] representation_model=representation_model,
[7] )
----> [8] topics, probs = topic_model.fit_transform(corpus, np.array(corpus_embeddings))
[9] topic_model.get_topic_info()

File ~/query_analysis/bertopic_env/lib/python3.11/site-packages/bertopic/_bertopic.py:493, in BERTopic.fit_transform(self, documents, embeddings, images, y)
[490] self._save_representative_docs(custom_documents)
[491] else:
[492] # Extract topics by calculating c-TF-IDF
--> [493] self._extract_topics(documents, embeddings=embeddings, verbose=self.verbose)
[495] # Reduce topics
[496] if self.nr_topics:

File ~/query_analysis/bertopic_env/lib/python3.11/site-packages/bertopic/_bertopic.py:3991, in BERTopic._extract_topics(self, documents, embeddings, mappings, verbose)
[3989] documents_per_topic = documents.groupby(["Topic"], as_index=False).agg({"Document": " ".join})
[3990] self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
-> [3991] self.topic_representations_ = self._extract_words_per_topic(words, documents)
...
[3680] "Make sure to use an embedding model that can either embed documents"
[3681] "or images depending on which you want to embed."
[3682]

AttributeError: 'NoneType' object has no attribute 'embed_documents'
```

### Reproduction

```python
from query import Query
import json
import numpy as np
from sklearn.cluster import DBSCAN
from bertopic import BERTopic
from openai import OpenAI
from bertopic.representation import KeyBERTInspired

representation_model = KeyBERTInspired()
topic_model = BERTopic(
calculate_probabilities=True,
min_topic_size=15,
embedding_model=None,
representation_model=representation_model,
)
topics, probs = topic_model.fit_transform(corpus, np.array(corpus_embeddings))
topic_model.get_topic_info()

```

### BERTopic Version

0.16.4

Start in bertopic/_bertopic.py at fit_transform, then follow _extract_topics into _extract_words_per_topic, where the reported AttributeError occurs with KeyBERTInspired and embedding_model=None. Reproduce the example using supplied corpus_embeddings and confirm that fitting with a representation_model no longer requires access to embedding_model.

Written by the indexing model from the issue text.