How to calculate adjusted mean and adjusted std error using lm()

#1
I'm using regression as ancova as below. Condition is coded: 0 = control, and 1 = experimental.

I understand that the intercept is the adjusted mean for the control and the intercept + raw condition coefficient is the adjusted mean for the experimental group.

My question is, can i calculate the SE of these adjusted means in the same way?

i.e. can i report this:
Control adj M (SE)
6.91 (2.55)

Experimental adj M (SE)
11.83 (3.85)

cheers

Tim

Call:
lm(formula = X.STAIpost. ~ X.STAIpre. + X.Condition.)

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.91183 2.55222 2.708 0.008039 **
X.STAIpre. 0.83641 0.06353 13.165 < 2e-16 ***
X.Condition. 4.92851 1.29853 3.795 0.000261 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.394 on 94 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.6647, Adjusted R-squared: 0.6576
F-statistic: 93.19 on 2 and 94 DF, p-value: < 2.2e-16