To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical model that can be implemented in Python. The objective function and constraints are defined as follows:

- Objective Function: Minimize \(3 \times (\text{security engineers})^2 + 3 \times (\text{security engineers}) \times (\text{patches per day}) + 3 \times (\text{automatic alerts})^2 + 3 \times (\text{automatic alerts}) \times (\text{patches per day}) + 2 \times (\text{patches per day})^2 + 6 \times (\text{automatic alerts})\)

- Constraints:
  1. Data confidentiality impact of security engineers is 10.
  2. Automatic alerts have a data confidentiality impact of 23 each.
  3. Patches per day each have a data confidentiality impact of 1.
  4. Total combined data confidentiality impact from security engineers plus automatic alerts should be at least 21.
  5. Total combined data confidentiality impact from security engineers squared, and patches per day squared must be as much or more than 35.
  6. Total combined data confidentiality impact from security engineers plus automatic alerts plus patches per day should be at minimum 35.
  7. \(10 \times (\text{security engineers}) - (\text{patches per day}) \geq 0\).
  8. The number of security engineers must be an integer.
  9. The number of automatic alerts must be a non-negative integer (since it cannot be fractional and typically, the number of alerts is not negative).
  10. The number of patches per day must be an integer.

Given these constraints and the objective function, we can now formulate this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Define variables
security_engineers = m.addVar(vtype=GRB.INTEGER, name="security_engineers")
automatic_alerts = m.addVar(vtype=GRB.INTEGER, name="automatic_alerts", lb=0)
patches_per_day = m.addVar(vtype=GRB.INTEGER, name="patches_per_day")

# 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)

# Constraints
m.addConstr(10 * security_engineers >= 10)  # Data confidentiality impact of security engineers
m.addConstr(23 * automatic_alerts >= 23 * automatic_alerts)  # Impact of automatic alerts
m.addConstr(patches_per_day >= patches_per_day)  # Each patch has an impact of 1

# Total combined data confidentiality impact constraints
m.addConstr(10 * security_engineers + 23 * automatic_alerts >= 21)
m.addConstr((10 * security_engineers)**2 + (patches_per_day)**2 >= 35)
m.addConstr(10 * security_engineers + 23 * automatic_alerts + patches_per_day >= 35)

# Additional constraint
m.addConstr(10 * security_engineers - patches_per_day >= 0)

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Security Engineers: {security_engineers.x}")
    print(f"Automatic Alerts: {automatic_alerts.x}")
    print(f"Patches per Day: {patches_per_day.x}")
else:
    print("No optimal solution found")
```