.merge_models() alters HDBSCAN clustering

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### Desribe the bug

I have 2 bertopic model with HDBSCAN configured similarly trained on 2 different subsets of data. However, when these models are merged the resulting merged model defaults to BaseCluster and bypasses the clustering when calling .transform()

### Reproduction

```python

umap_mode1l = UMAP(n_components=25, metric='cosine', random_state=42)
vectorizer_model1 = CountVectorizer(stop_words="english")

model1 = BERTopic(umap_model=umap_model1,
vectorizer_model=vectorizer_model1,
calculate_probabilities=True,
verbose=True)
model1.fit(data1, embeddings=embeddings1)

umap_model2 = UMAP(n_components=25, metric='cosine', random_state=42)
vectorizer_model2 = CountVectorizer(stop_words="english")
model2 = BERTopic(umap_model=umap_model2,
vectorizer_model=vectorizer_model2,
calculate_probabilities=True,
verbose=True)
model2.fit(data2, embeddings=embeddings2)

merged_model = BERTopic.merge_models([model1, model2],
min_similarity=0.7)

merged_model.hdbscan_model

```

### BERTopic Version

0.17.3

Start by reproducing the report with the provided BERTopic.merge_models([model1, model2], min_similarity=0.7) example in BERTopic 0.17.3. Inspect merged_model.hdbscan_model and then follow the .transform() call to determine why merging replaces or bypasses HDBSCAN. Done means a merged model preserves the expected clustering behavior during transform.

Written by the indexing model from the issue text.