The 'X' parameter of normalize must be an array-like or a sparse matrix.

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### Desribe the bug

I have a very large dataset, so I have been saving and loading models as is documented a good practice elsewhere. The problem is, when I load and merge these, I cannot do the class-based or time-dependent topic representations, as it seems c-tf-idf information is not saved when a model is saved to disk in the model.

Full error log:

```
Traceback (most recent call last):
File "/Users/ysc4337/warlock/merging-test/main.py", line 56, in
topics_over_time = merged_model.topics_over_time(all_docs, all_timestamps)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/ysc4337/warlock/merging-test/.venv/lib/python3.11/site-packages/bertopic/_bertopic.py", line 860, in topics_over_time
global_c_tf_idf = normalize(self.c_tf_idf_, axis=1, norm="l1", copy=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/ysc4337/warlock/merging-test/.venv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py", line 206, in wrapper
validate_parameter_constraints(
File "/Users/ysc4337/warlock/merging-test/.venv/lib/python3.11/site-packages/sklearn/utils/_param_validation.py", line 98, in validate_parameter_constraints
raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'X' parameter of normalize must be an array-like or a sparse matrix. Got None instead.
(merging-test) ➜ merging-test git:(master) ✗
```

I have tried saving the c_tf_idf matrix as a workaround, but I could not get the code working.

### Reproduction

```python
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
import random
from datetime import datetime, timedelta

# Fetch the dataset
data = fetch_20newsgroups(subset="all")
documents = data.data
categories = data.target

# Generate synthetic timestamps
start_date = datetime(2000, 1, 1)
timestamps = [
str(start_date + timedelta(days=random.randint(0, 7300)))
for _ in range(len(documents))
]

# Combine data with timestamps and categories
data_with_metadata = list(zip(documents, timestamps, categories))

# Split data into two parts for training two separate models
data_part1 = data_with_metadata[: len(data_with_metadata) // 2]
data_part2 = data_with_metadata[len(data_with_metadata) // 2 :]

# Extract documents and timestamps from both parts
docs_part1 = [doc for doc, _, _ in data_part1]
docs_part2 = [doc for doc, _, _ in data_part2]
timestamps_part1 = [ts for _, ts, _ in data_part1]
timestamps_part2 = [ts for _, ts, _ in data_part2]

model1 = BERTopic(verbose=True)
model2 = BERTopic(verbose=True)

topics, _ = model1.fit_transform(docs_part1)
topics, _ = model2.fit_transform(docs_part2)

model1.save("bertopic_model1")
model2.save("bertopic_model2")

model1 = BERTopic.load("bertopic_model1")
model2 = BERTopic.load("bertopic_model2")
# Merge the models

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

# Reload the models

# Combine all documents and timestamps
all_docs = docs_part1 + docs_part2
all_timestamps = timestamps_part1 + timestamps_part2

# Fit the merged model to generate the c-TF-IDF matrix

# Generate topics over time visualization
topics_over_time = merged_model.topics_over_time(all_docs, all_timestamps)
merged_model.visualize_topics_over_time(topics_over_time).write_html(
"topics_over_time.html"
)
```

### BERTopic Version

0.17.0

Start with bertopic/_bertopic.py, especially BERTopic.save, load, merge_models, and topics_over_time, then run the provided 20 Newsgroups reproduction. Trace why merged_model.c_tf_idf_ is None after loading and merging; done means the reproduced topics_over_time call no longer raises the normalize error.

Written by the indexing model from the issue text.