There are two problems here, and they both have to do with defining orderings for vectors.
The first is deciding what it would even mean to do a one-sided test. Suppose you have just two parameters $\beta_x$ and $\beta_y$ and you want to test $\leq 0$ vs $>0$. Do you want to reject when one is positive and you're not sure about the other or do they both have to be positive? The possibilities grow exponentially with the number of tests.
The second problem is technical. The theory for the Wald multi-parameter test doesn't apply to null hypotheses with corners. Suppose your alternative is $\beta_x$ and $\beta_y$ are both positive and your null is that at least one of them is zero or negative. The boundary of the null hypothesis is made up of two line segments that meet at right angles at the origin. The distribution of the Wald test statistic will not be the usual $\chi^2$ distribution; it will be a mixture of $\chi^2$ distributions.
Both these problems can be overcome, but they mean there isn't a simple generalisation of the one-sided tests to multiple parameters.