Here's the Gurobi code that represents the optimization problem you described:

```python
from gurobipy import Model, GRB

# Create a new model
m = Model("optimization_problem")

# Create variables
security_engineers = m.addVar(vtype=GRB.INTEGER, name="security_engineers")
automatic_alerts = m.addVar(vtype=GRB.INTEGER, name="automatic_alerts")
patches_per_day = m.addVar(vtype=GRB.INTEGER, name="patches_per_day")

# Set objective function
m.setObjective(3 * security_engineers**2 + 3 * security_engineers * patches_per_day + 3 * automatic_alerts**2 + 3 * automatic_alerts * patches_per_day + 2 * patches_per_day**2 + 6 * automatic_alerts, GRB.MINIMIZE)

# Add constraints
m.addConstr(10 * security_engineers + 23 * automatic_alerts >= 21, "data_confidentiality_impact1")
m.addConstr(10**2 * security_engineers**2 + 1**2 * patches_per_day**2 >= 35, "data_confidentiality_impact2")
m.addConstr(10 * security_engineers + 23 * automatic_alerts + 1 * patches_per_day >= 35, "data_confidentiality_impact3")
m.addConstr(10 * security_engineers - 1 * patches_per_day >= 0, "constraint4")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('security_engineers:', security_engineers.x)
    print('automatic_alerts:', automatic_alerts.x)
    print('patches_per_day:', patches_per_day.x)
elif m.status == GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```
