Topic names not set to zero-shot topic when `fit_transform` results in only zero-shot topics

Nobody has claimed this yet.

### Have you searched existing issues? 🔎

- [x] I have searched and found no existing issues

### Desribe the bug

The names for the topics are not set to the zero-shot topic names when the `fit_transform` results in only zero-shot topics .

If the result contains zero-shot topics and non-zero-shot topics the zero-shot topics get their name from `zeroshot_topic_list`. However when there are only zero-shot topics (by using a very low `zeroshot_min_similarity` for example) all documents get assigned to zero-shot topics, but the names are not taken from the list.

**Name for topic when running reproduction code:** 0_optimization_prediction_algorithms_optimal
**Expected name for topic:** Clustering

[Looking at the code](https://github.com/MaartenGr/BERTopic/blob/0bed25cdac0445fe01869e47b3b0beeaf124230e/bertopic/_bertopic.py#L489C1-L499C45), when not all documents are assigned to zeroshot topics `len(documents) > 0` is true and `combine_zeroshot_topics` is called where the topic/label mapping is updated. When all documents have been assigned this function is not called and the mapping is not updated.
```python
if len(documents) > 0:
# Cluster reduced embeddings
documents, probabilities = self._cluster_embeddings(umap_embeddings, documents, y=y)
if self._is_zeroshot() and len(assigned_documents) > 0:
documents, embeddings = self._combine_zeroshot_topics(
documents, embeddings, assigned_documents, assigned_embeddings
)
else:
# All documents matches zero-shot topics
documents = assigned_documents
embeddings = assigned_embeddings
```

### Reproduction

```python
from datasets import load_dataset

from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired

# We select a subsample of 100 abstracts from ArXiv
dataset = load_dataset("CShorten/ML-ArXiv-Papers")["train"]
docs = dataset["abstract"][:100]

# We define a number of topics that we know are in the documents
zeroshot_topic_list = ["Clustering"]

# We fit our model using the zero-shot topics
# and we define a minimum similarity. For each document,
# if the similarity does not exceed that value, it will be used
# for clustering instead.
topic_model = BERTopic(
embedding_model="thenlper/gte-small",
min_topic_size=15,
zeroshot_topic_list=zeroshot_topic_list,
zeroshot_min_similarity=.000001, # Low value to make sure all documents are assigned to zero-shot topics
representation_model=KeyBERTInspired()
)
topics, _ = topic_model.fit_transform(docs)
print(topic_model.get_topic_info())

```

### BERTopic Version

0.17.3

Start in bertopic/_bertopic.py around the fit_transform branch linked in the issue, especially the all-zero-shot-documents path and _combine_zeroshot_topics. Run the supplied reproduction with BERTopic 0.17.3 and inspect get_topic_info(); done means the sole zero-shot topic uses the name "Clustering" when every document is assigned to it.

Written by the indexing model from the issue text.