```json
{
  "sym_variables": [
    ("x0", "patches per day"),
    ("x1", "pen testers"),
    ("x2", "intrusion analysts")
  ],
  "objective_function": "7.19 * x0 * x1 + 1.57 * x0 * x2 + 2.02 * x1**2 + 3.09 * x0 + 7.49 * x1",
  "constraints": [
    "11.44 * x0 + 11.11 * x1 + 14.9 * x2 <= 84",
    "12.65 * x0 + 0.78 * x1 + 14.19 * x2 <= 155",
    "x0 + x2 <= 65",
    "11.44 * x0 + 11.11 * x1 + 14.9 * x2 <= 65",
    "12.65 * x0 + 14.19 * x2 <= 69",
    "0.78 * x1 + 14.19 * x2 <= 105",
    "12.65 * x0 + 0.78 * x1 <= 107",
    "12.65 * x0 + 0.78 * x1 + 14.19 * x2 <= 131",
    "x0 == int(x0)",
    "x1 == int(x1)",
    "x2 == int(x2)"

  ]
}
```

```python
import gurobipy as gp
from gurobipy import GRB

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

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

    # Set objective function
    m.setObjective(7.19 * patches_per_day * pen_testers + 1.57 * patches_per_day * intrusion_analysts + 2.02 * pen_testers**2 + 3.09 * patches_per_day + 7.49 * pen_testers, GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(11.44 * patches_per_day + 11.11 * pen_testers + 14.9 * intrusion_analysts <= 84, "power_consumption_limit1")
    m.addConstr(12.65 * patches_per_day + 0.78 * pen_testers + 14.19 * intrusion_analysts <= 155, "data_integrity_impact_limit1")
    m.addConstr(patches_per_day + intrusion_analysts <= 65, "power_consumption_limit2")
    m.addConstr(11.44 * patches_per_day + 11.11 * pen_testers + 14.9 * intrusion_analysts <= 65, "power_consumption_limit3")
    m.addConstr(12.65 * patches_per_day + 14.19 * intrusion_analysts <= 69, "data_integrity_impact_limit2")
    m.addConstr(0.78 * pen_testers + 14.19 * intrusion_analysts <= 105, "data_integrity_impact_limit3")
    m.addConstr(12.65 * patches_per_day + 0.78 * pen_testers <= 107, "data_integrity_impact_limit4")
    m.addConstr(12.65 * patches_per_day + 0.78 * pen_testers + 14.19 * intrusion_analysts <= 131, "data_integrity_impact_limit5")


    # Optimize model
    m.optimize()

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


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

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