StandardScaler and PolynomialFeatures fail on zero-feature inputs during fit (should become passthrough)

### Describe the bug

If you use StandardScaler or PolynomialFeatures (or other transformers, these are the two that hit me first) as elements in  a complex pipeline, an issue comes up if you ever fit these transformers on data that has no features (empty data). This happens a lot when you build a complex pipeline and you don't know what the features will be in advance. For example, in reinforcement learning problems, some problems will have context features while others won't, and we need pipelines for both the action and context features which are then concatenated (using FeatureUnion). My current workaround is that when I try to fit my pipeline, if it fails, it eliminates StandardScalers and PolynomialFeatures because they are a part of the context pipeline and we don't have context features for this problem, then fit again. 

### Steps/Code to Reproduce


from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('polynomial', PolynomialFeatures()),
    ('fully_transformed_action_and_context', StandardScaler()),
])

pipeline.fit([[]])

### Expected Results

No error is thrown. Pipeline becomes equivalent to passthrough.

### Actual Results

  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/pipeline.py", line 390, in fit
    Xt = self._fit(X, y, **fit_params_steps)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/pipeline.py", line 348, in _fit
    X, fitted_transformer = fit_transform_one_cached(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/joblib/memory.py", line 355, in __call__
    return self.func(*args, **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/pipeline.py", line 893, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/base.py", line 847, in fit_transform
    return self.fit(X, **fit_params).transform(X)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/preprocessing/_polynomial.py", line 287, in fit
    _, n_features = self._validate_data(X, accept_sparse=True).shape
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/base.py", line 561, in _validate_data
    X = check_array(X, **check_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py", line 806, in check_array
    raise ValueError(
ValueError: Found array with 0 feature(s) (shape=(1, 0)) while a minimum of 1 is required.


### Versions

```shell
System:
    python: 3.8.2 (v3.8.2:7b3ab5921f, Feb 24 2020, 17:52:18)  [Clang 6.0 (clang-600.0.57)]
executable: /usr/local/bin/python3.8
   machine: macOS-10.15.7-x86_64-i386-64bit
Python dependencies:
          pip: 20.0.2
   setuptools: 41.2.0
      sklearn: 1.0.1
        numpy: 1.19.5
        scipy: 1.4.1
       Cython: 0.29.16
       pandas: 1.4.0
   matplotlib: 3.2.1
       joblib: 0.14.1
threadpoolctl: 3.0.0
Built with OpenMP: True
```

COMMENT:
For example, a lot of the reason you use the fit method on a pipeline is precisely because you don’t know in advance how many features you will have at any step in the pipeline. Having to code special cases for “zero” output features makes the code unwieldy and difficult to work with.

Labels: Bug, module:preprocessing