Yes; reparameterize it as $\beta_2=\beta_1+\delta$, so that your predictors are no longer $x_1,x_2$ but $x_1^*=x_1+x_2$ (to go with $\beta_1$) and $x_2$ (to go with $\delta$)
[Note that $\delta = \beta_2-\beta_1$, and also $\hat{\delta}=\hat{\beta}_2-\hat{\beta}_1$; further, $\text{Var}(\hat\delta)$ will be correct relative to the original.]
Then test the null of $\delta=0$ against the alternative of $\delta<0$.
[Alternatively, identify the matrix $C$ defining the linear restriction under the null and test the general linear hypothesis $C\beta=0$; for example, see the extensive description via F or t tests here. Since your alternative is one-tailed, you'll want the t-form.]