Feature importance with Text features
permutation_importance is considering the top-level features. It is permuting each one sequentially and learning the importance.
So, the inner encoding i.e. OHE/tfid is not visible to it.
To get the importance of components of the top-level feature, you should encode it separately and then pass the encoded data to the permutation_importance

Get the pre-processed data using preprocessing_cv.fit_transform(X_train)
Call your permutation_importance code on the above data and any model of your choice

Edit
Adding the snippet. I am excluding ColumnTranformer as it is causing some issue.
data = {'Number':[1,2,3], 'Text':['pen is table', 'sky is blue','Sun is kool'], 'Cat':['A','B', 'C']}
df = pd.DataFrame(data)

categorical_preprocessing = OneHotEncoder(handle_unknown='ignore')
numeric_preprocessing = SimpleImputer(strategy='mean')
text_preprocessing_cv =  CountVectorizer()

text_tfid = text_preprocessing_cv.fit_transform(df['Text']).toarray()
num = numeric_preprocessing.fit_transform(df['Number'].values.reshape(-1, 1))
cat = categorical_preprocessing.fit_transform(df['Cat'].values.reshape(-1, 1)).toarray()

data = np.concatenate((cat,num,text_tfid), axis=1)
cols =  np.concatenate((categorical_preprocessing.get_feature_names(), text_preprocessing_cv.get_feature_names(), ['Num'])) # New cols name

df = pd.DataFrame(data, columns=cols) # Encoded DataFrame with col name


from sklearn.inspection import permutation_importance
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier().fit(df, [1,0,1])

result = permutation_importance(clf, df, [1,0,1], n_repeats=2, random_state=42)
sorted_idx = result.importances_mean.argsort()

plt.boxplot(result.importances[sorted_idx].T,
            vert=False, labels=df.columns[sorted_idx]);