Multinomial Naive bayes giving wrong result
There is only one feature for xx.

For a MultinomialNB, the modelled probability of the feature per class $p(x_i|y)$ is always the same ($\frac{3}{3}=1$ for class $1$ and $\frac{7}{7}=1$ for class $0$). The prediction is therefore the class prior $p(y_i)$ which is also the same here ($p(0) = 0.5$ and $p(1)=0.5$) so by default class $0$. Having one feature does not really makes sense because it gives no information to the model on what gives discriminative power toward the classes.

For a GaussianNB this is different because the modelled probability is given by $P(x_i \mid y) = \frac{1}{\sqrt{2\pi\sigma^2_y}} \exp\left(-\frac{(x_i - \mu_y)^2}{2\sigma^2_y}\right)$ so the mean and variance per class of the single feature give sufficient information.