Loading problems of Datasets with a single shard
### Describe the bug

When saving a dataset on disk and it has a single shard it is not loaded as when it is saved in multiple shards. I installed the latest version of datasets via pip. 

### Steps to reproduce the bug

The code below reproduces the behavior. All works well when the range of the loop is 10000 but it fails when it is 1000.

```
from PIL import Image
import numpy as np
from datasets import Dataset, DatasetDict, load_dataset


def load_image():
    # Generate random noise image
    noise = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)  
    return Image.fromarray(noise)

def create_dataset():
    input_images = []
    output_images = []
    text_prompts = []
  
    for _ in range(10000):  # this is the problematic parameter
        input_images.append(load_image())
        output_images.append(load_image())
        text_prompts.append('test prompt')

    data = {'input_image': input_images, 'output_image': output_images, 'text_prompt': text_prompts}
    dataset = Dataset.from_dict(data)
    
    return DatasetDict({'train': dataset})

dataset = create_dataset()

print('dataset before saving')
print(dataset)
print(dataset['train'].column_names)
dataset.save_to_disk('test_ds')
print('dataset after loading')


dataset_loaded = load_dataset('test_ds')
print(dataset_loaded)
print(dataset_loaded['train'].column_names)
```


The output for 1000 iterations is: 

```
dataset before saving
DatasetDict({
    train: Dataset({
        features: ['input_image', 'output_image', 'text_prompt'],
        num_rows: 1000
    })
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (1/1 shards): 100%|█| 1000/1000 [00:00<00:00, 5156.00 example
dataset after loading
Generating train split: 1 examples [00:00, 230.52 examples/s]
DatasetDict({
    train: Dataset({
        features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],
        num_rows: 1
    })
})
['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split']
```

For 10000 iteration (8 shards) it is correct:

```
dataset before saving
DatasetDict({
    train: Dataset({
        features: ['input_image', 'output_image', 'text_prompt'],
        num_rows: 10000
    })
})
['input_image', 'output_image', 'text_prompt']
Saving the dataset (8/8 shards): 100%|█| 10000/10000 [00:01<00:00, 6237.68 examp
dataset after loading
Generating train split: 10000 examples [00:00, 10773.16 examples/s]
DatasetDict({
    train: Dataset({
        features: ['input_image', 'output_image', 'text_prompt'],
        num_rows: 10000
    })
})
['input_image', 'output_image', 'text_prompt']
```

### Expected behavior

The procedure should work for a dataset with one shrad the same as for one with multiple shards

### Environment info


- `datasets` version: 2.18.0
- Platform: macOS-14.1-arm64-arm-64bit
- Python version: 3.11.8
- `huggingface_hub` version: 0.22.2
- PyArrow version: 15.0.2
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0

Edit: I looked in the source code of load.py in datasets. I should have used "load_from_disk" and it indeed works that way. But ideally load_dataset would have raisen an error the same way as if I call a path: 

```
    if Path(path, config.DATASET_STATE_JSON_FILENAME).exists():
        raise ValueError(
            "You are trying to load a dataset that was saved using `save_to_disk`. "
            "Please use `load_from_disk` instead."
        )
```
nevertheless I find it interesting that it works just well and without a warning if there are multiple shards. 