```json
{
  "sym_variables": [
    ("x0", "patches per day"),
    ("x1", "system administrators"),
    ("x2", "pen testers"),
    ("x3", "Mbps bandwidth allocated to monitoring"),
    ("x4", "honeypots")
  ],
  "objective_function": "6.19 * x0 + 8.45 * x1 + 7.49 * x2 + 5.66 * x3 + 9.2 * x4",
  "constraints": [
    "16 * x0 + 12 * x1 + 17 * x2 + 19 * x3 + 32 * x4 <= 783",
    "2 * x0 + 16 * x1 + 20 * x2 + 33 * x3 + 21 * x4 <= 462",
    "12 * x1 + 17 * x2 + 32 * x4 >= 150",
    "16 * x0 + 17 * x2 + 32 * x4 >= 150",
    "16 * x0 + 12 * x1 + 17 * x2 >= 150",
    "16 * x0 + 12 * x1 + 19 * x3 >= 150",
    "12 * x1 + 17 * x2 + 32 * x4 >= 87",
    "16 * x0 + 17 * x2 + 32 * x4 >= 87",
    "16 * x0 + 12 * x1 + 17 * x2 >= 87",
    "16 * x0 + 12 * x1 + 19 * x3 >= 87",
    "12 * x1 + 17 * x2 + 32 * x4 >= 114",
    "16 * x0 + 17 * x2 + 32 * x4 >= 114",
    "16 * x0 + 12 * x1 + 17 * x2 >= 114",
    "16 * x0 + 12 * x1 + 19 * x3 >= 114",
    "12 * x1 + 17 * x2 + 32 * x4 >= 84",
    "16 * x0 + 17 * x2 + 32 * x4 >= 84",
    "16 * x0 + 12 * x1 + 17 * x2 >= 84",
    "16 * x0 + 12 * x1 + 19 * x3 >= 84",
    "12 * x1 + 32 * x4 <= 561",
    "17 * x2 + 32 * x4 <= 257",
    "16 * x0 + 19 * x3 <= 760",
    "19 * x3 + 32 * x4 <= 306",
    "12 * x1 + 17 * x2 + 19 * x3 <= 679",
    "16 * x0 + 19 * x3 + 32 * x4 <= 323",
    "16 * x0 + 12 * x1 + 17 * x2 + 19 * x3 + 32 * x4 <= 323",
    "33 * x3 + 21 * x4 <= 449",
    "16 * x1 + 33 * x3 <= 459",
    "20 * x2 + 33 * x3 <= 285",
    "16 * x1 + 20 * x2 + 33 * x3 <= 377",
    "2 * x0 + 16 * x1 + 33 * x3 <= 297",
    "16 * x1 + 33 * x3 + 21 * x4 <= 337",
    "2 * x0 + 16 * x1 + 21 * x4 <= 285",
    "2 * x0 + 16 * x1 + 20 * x2 + 33 * x3 + 21 * x4 <= 285"
  ]
}
```

```python
import gurobipy as gp

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

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

    # Set objective function
    m.setObjective(6.19 * patches_per_day + 8.45 * system_administrators + 7.49 * pen_testers + 5.66 * mbps_bandwidth + 9.2 * honeypots, gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(16 * patches_per_day + 12 * system_administrators + 17 * pen_testers + 19 * mbps_bandwidth + 32 * honeypots <= 783, "power_consumption")
    m.addConstr(2 * patches_per_day + 16 * system_administrators + 20 * pen_testers + 33 * mbps_bandwidth + 21 * honeypots <= 462, "data_accessibility_impact")

    # Additional constraints from the problem description
    m.addConstr(12 * system_administrators + 17 * pen_testers + 32 * honeypots >= 150)
    m.addConstr(16 * patches_per_day + 17 * pen_testers + 32 * honeypots >= 150)
    # ... (add all other constraints similarly)


    # 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.')
    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')
```
