why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?
I do not think your formulation is correct. What you have described are just conditional distributions for each word in the sentence but not the joint conditional distribution, given a specific class.

In your case, we have by Bayes rule:

$$ Pr(spam | X) \propto Pr(X | spam) \times Pr(spam) = Pr(you, won, lottery, for, 1million | spam) \times Pr(spam).$$ 

Now, $\hat Pr(spam) = \frac{\text{# of spam occurences in train}}{\text{total observations in train}}$, the MLE that uses none of the features.

In Naive Bayes, we make the assumption that:

$$Pr(you, won, lottery, for, 1million | spam) \times Pr(spam) = Pr(you | spam) * Pr(won | spam) * Pr(lottery | spam) * Pr(for|spam) * Pr(1million | spam) * Pr(spam).$$ That is, we assumed that each of the features are conditionally independent given a class. If we do not wish to make this assumption, we simply leave $Pr(you, won, lottery, for, 1million | spam)$ as is in the above expression and posit some sort of multivariate/joint conditional distribution.

The reason why we make this assumption is due to the difficulty in determining what the conditional joint distribution of the features is given a particular class. Is the joint distribution a multivariate Poisson? A complex mixture of many? Some other multivariate counting distribution? What is the dependence structure, and how should we represent it? What if we have a thousand features? How do we represent the dependence structure now in a reasonable way? Is the dependence structure so complicated that representing it reasonably is more annoying and/or time-consuming than just assuming independence?

These questions are not trivial and are very difficult, if not impossible, to answer. Thus, we make the independence assumption to simplify the problem. We may not know the joint conditional distribution but perhaps we can derive and/or visualize the marginal conditional distributions for each feature. This, of course, comes at the cost of assuming independence between features which is highly unlikely to be true (especially for word frequencies) but nevertheless occasionally effective for some problems (kind of).