In order to calculate the variance of $\bar{X} - \bar{Y}$ you need to know something about the covariance between $X$ and $Y$. If you have the original data then you can estimate the covariance directly, but absent this information we can use the Cauchy-Schwarz inequality to get an upper bound:
\begin{align}
\text{Var} \left (\bar{X} - \bar{Y} \right ) &= \sigma_x^2 / n + \sigma_y^2 / n - 2 \sigma_{\bar{x}\bar{y}} \\
&\leq \sigma_x^2 / n + \sigma_\bar{y}^2 / n + 2 | \sigma_{\bar{x}\bar{y}} | \\
&\leq \sigma_x^2 / n + \sigma_y^2 / n + 2 \sigma_x \sigma_y / n \\
&= (\sigma_x + \sigma_y)^2 / n,
\end{align}
which can be estimated by plugging in the appropriate point estimates. This could potentially be quite a bit larger than the actual variance as it's only achieved when $X$ and $Y$ are perfectly negatively correlated, but it's the best you can do without more information.