reduce_outliers and update_topics remove stop_words and ngram_range effects

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### Desribe the bug

After running `reduce_outliers` and `update_topics`, the effects of all specifications used in `vectorizer_model` (stop words, ngram) are gone. The results' representation words only show single words. Thanks.

```
vectorizer_model = CountVectorizer(stop_words=stop_words, ngram_range=(1, 4), min_df=5)
representation_model = MaximalMarginalRelevance(diversity=0.5)

topic_model_outlier_reduction = BERTopic(
vectorizer_model=vectorizer_model,
representation_model=representation_model,
top_n_words=15,
min_topic_size=15,
calculate_probabilities=True
)
topics_outlier_reduction, probs_outlier_reduction = topic_model_outlier_reduction.fit_transform(docs, embeddings)

new_topics = topic_model_outlier_reduction.reduce_outliers(docs,
topics_outlier_reduction,
threshold=0.2, strategy="distributions") # probabilities=probs_outlier_reduction,

topic_model_outlier_reduction.update_topics(docs, topics=new_topics)
```

### BERTopic Version

0.16.0

Start at the reduce_outliers and update_topics entry points and trace how the configured CountVectorizer is used after topics are reassigned. Reproduce the example with stop_words and ngram_range=(1, 4); done means updated topic representations preserve those vectorizer effects rather than showing only single words.

Written by the indexing model from the issue text.