CountVectorizer in Python
You could easily just concatenate these matrices and other feature columns to build one very large matrix.
However, be aware that concatenating the matrix from email body and email subject will probably create an incredibly sparse matrix. When you then add other features you might risk to "water down" your other features. This depends mostly on the algorithm you choose to use for your prediction. 
In all cases you would benefit from reducing the dimensionality of the two matrices for email subject and body or directly use a different approach than CountVectorizer - for example TFIDF.
I am working on a problem in which I have to predict whether a sent email from a company is opened or not and if it is opened, I have to predict whether the recipient clicked on the given link or not.
I have a data set with the following features:

Total links inside the emai`
Total internal links inside the
email 
Number of images inside the email 
Number of sections
inside the email 
Email_body 
Email Subject

For the email body and subject I can use a CountVectorizer but how can I include my other feature to that sparse matrix created by said CountVectorizer.