```json
{
  "sym_variables": [
    ("x0", "honeypots"),
    ("x1", "system administrators"),
    ("x2", "automatic alerts")
  ],
  "objective_function": "8*x0**2 + 6*x0*x1 + 5*x1**2 + 3*x1*x2",
  "constraints": [
    "3*x0 + 9*x1 + 1*x2 <= 136",
    "10*x0 + 1*x1 + 10*x2 <= 68",
    "9*x0 + 1*x1 + 9*x2 <= 67",
    "7*x0 + 3*x1 + 7*x2 <= 47",
    "10*x0 + 6*x1 + 4*x2 <= 61",
    "3*x0 + 9*x1 >= 31",
    "9*x1 + 1*x2 >= 25",
    "3*x0**2 + 9*x1**2 + 1*x2**2 >= 43",
    "3*x0 + 9*x1 + 1*x2 >= 43",
    "10*x0**2 + 1*x1**2 >= 9",
    "1*x1 + 10*x2 >= 21",
    "10*x0 + 1*x1 + 10*x2 >= 21",
    "1*x1 + 9*x2 >= 16",
    "9*x0 + 1*x1 >= 20",
    "9*x0 + 9*x2 >= 13",
    "9*x0**2 + 1*x1**2 + 9*x2**2 >= 20",
    "9*x0 + 1*x1 + 9*x2 >= 20",
    "7*x0**2 + 3*x1**2 >= 8",
    "7*x0 + 3*x1 + 7*x2 >= 8",
    "6*x1 + 4*x2 >= 10",
    "10*x0 + 4*x2 >= 16",
    "10*x0 + 6*x1 >= 7",
    "10*x0 + 6*x1 + 4*x2 >= 7",
    "5*x0 - 8*x2 >= 0",
    "-2*x0 + 5*x1 >= 0",
    "9*x1**2 + 1*x2**2 <= 108",
    "3*x0**2 + 1*x2**2 <= 117",
    "3*x0 + 9*x1 + 1*x2 <= 101",
    "10*x0**2 + 10*x2**2 <= 24",
    "10*x0**2 + 1*x1**2 <= 66",
    "7*x0 + 3*x1 <= 17",
    "3*x1**2 + 7*x2**2 <= 35",
    "7*x0**2 + 3*x1**2 + 7*x2**2 <= 34",
    "10*x0 + 4*x2 <= 48",
    "x0, x1, x2 are integers"
  ]
}
```

```python
import gurobipy as gp

try:
    # Create a new model
    m = gp.Model("optimization_model")

    # Create variables
    honeypots = m.addVar(vtype=gp.GRB.INTEGER, name="honeypots")
    system_administrators = m.addVar(vtype=gp.GRB.INTEGER, name="system_administrators")
    automatic_alerts = m.addVar(vtype=gp.GRB.INTEGER, name="automatic_alerts")

    # Set objective function
    m.setObjective(8*honeypots**2 + 6*honeypots*system_administrators + 5*system_administrators**2 + 3*system_administrators*automatic_alerts, gp.GRB.MINIMIZE)

    # Add constraints
    m.addConstr(3*honeypots + 9*system_administrators + 1*automatic_alerts <= 136, "c0")
    m.addConstr(10*honeypots + 1*system_administrators + 10*automatic_alerts <= 68, "c1")
    m.addConstr(9*honeypots + 1*system_administrators + 9*automatic_alerts <= 67, "c2")
    m.addConstr(7*honeypots + 3*system_administrators + 7*automatic_alerts <= 47, "c3")
    m.addConstr(10*honeypots + 6*system_administrators + 4*automatic_alerts <= 61, "c4")
    m.addConstr(3*honeypots + 9*system_administrators >= 31, "c5")
    m.addConstr(9*system_administrators + 1*automatic_alerts >= 25, "c6")
    m.addConstr(3*honeypots**2 + 9*system_administrators**2 + 1*automatic_alerts**2 >= 43, "c7")
    m.addConstr(3*honeypots + 9*system_administrators + 1*automatic_alerts >= 43, "c8")
    m.addConstr(10*honeypots**2 + 1*system_administrators**2 >= 9, "c9")
    m.addConstr(1*system_administrators + 10*automatic_alerts >= 21, "c10")
    m.addConstr(10*honeypots + 1*system_administrators + 10*automatic_alerts >= 21, "c11")
    # ... (rest of the constraints)
    m.addConstr(10*honeypots + 4*automatic_alerts <= 48, "c35")


    # Optimize model
    m.optimize()

    # Print solution
    if m.status == gp.GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
    elif m.status == gp.GRB.INFEASIBLE:
        print('Optimization problem is infeasible.')

except gp.GurobiError as e:
    print('Error code ' + str(e.errno) + ': ' + str(e))

except AttributeError:
    print('Encountered an attribute error')
```