```json
{
  "sym_variables": [
    ("x0", "hours worked by Bill"),
    ("x1", "hours worked by Hank"),
    ("x2", "hours worked by Bobby"),
    ("x3", "hours worked by John")
  ],
  "objective_function": "9*x0 + 3*x1 + 2*x2 + 2*x3",
  "constraints": [
    "6*x0 + 6*x1 + 11*x3 >= 31",
    "6*x0 + 6*x1 + 7*x2 >= 31",
    "6*x0 + 6*x1 + 11*x3 >= 37",
    "6*x0 + 6*x1 + 7*x2 >= 37",
    "9*x1 + 18*x2 >= 17",
    "9*x1 + 12*x3 >= 20",
    "8*x0 + 9*x1 >= 27",
    "18*x2 + 12*x3 >= 19",
    "7*x0 + 1*x2 + 13*x3 >= 26",
    "7*x0 + 4*x1 + 1*x2 >= 26",
    "7*x0 + 1*x2 + 13*x3 >= 27",
    "7*x0 + 4*x1 + 1*x2 >= 27",
    "7*x2 + 11*x3 <= 99",
    "6*x0 + 11*x3 <= 52",
    "6*x0 + 7*x2 <= 118",
    "6*x0 + 7*x2 + 11*x3 <= 60",
    "6*x0 + 6*x1 + 7*x2 + 11*x3 <= 60",
    "8*x0 + 18*x2 <= 104",
    "9*x1 + 18*x2 <= 63",
    "8*x0 + 12*x3 <= 34",
    "8*x0 + 9*x1 <= 72",
    "8*x0 + 9*x1 + 18*x2 + 12*x3 <= 72",
    "7*x0 + 4*x1 <= 64",
    "7*x0 + 1*x2 <= 71",
    "7*x0 + 13*x3 <= 106",
    "4*x1 + 13*x3 <= 29",
    "7*x0 + 4*x1 + 1*x2 + 13*x3 <= 29"
  ]
}
```

```python
import gurobipy as gp

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

    # Create variables
    x = m.addVars(4, lb=0, vtype=gp.GRB.CONTINUOUS, name=["Bill", "Hank", "Bobby", "John"])


    # Set objective function
    m.setObjective(9*x[0] + 3*x[1] + 2*x[2] + 2*x[3], gp.GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(6*x[0] + 6*x[1] + 11*x[3] >= 31)
    m.addConstr(6*x[0] + 6*x[1] + 7*x[2] >= 31)
    m.addConstr(6*x[0] + 6*x[1] + 11*x[3] >= 37)
    m.addConstr(6*x[0] + 6*x[1] + 7*x[2] >= 37)
    m.addConstr(9*x[1] + 18*x[2] >= 17)
    m.addConstr(9*x[1] + 12*x[3] >= 20)
    m.addConstr(8*x[0] + 9*x[1] >= 27)
    m.addConstr(18*x[2] + 12*x[3] >= 19)
    m.addConstr(7*x[0] + 1*x[2] + 13*x[3] >= 26)
    m.addConstr(7*x[0] + 4*x[1] + 1*x[2] >= 26)
    m.addConstr(7*x[0] + 1*x[2] + 13*x[3] >= 27)
    m.addConstr(7*x[0] + 4*x[1] + 1*x[2] >= 27)
    m.addConstr(7*x[2] + 11*x[3] <= 99)
    m.addConstr(6*x[0] + 11*x[3] <= 52)
    m.addConstr(6*x[0] + 7*x[2] <= 118)
    m.addConstr(6*x[0] + 7*x[2] + 11*x[3] <= 60)
    m.addConstr(6*x[0] + 6*x[1] + 7*x[2] + 11*x[3] <= 60)
    m.addConstr(8*x[0] + 18*x[2] <= 104)
    m.addConstr(9*x[1] + 18*x[2] <= 63)
    m.addConstr(8*x[0] + 12*x[3] <= 34)
    m.addConstr(8*x[0] + 9*x[1] <= 72)
    m.addConstr(8*x[0] + 9*x[1] + 18*x[2] + 12*x[3] <= 72)
    m.addConstr(7*x[0] + 4*x[1] <= 64)
    m.addConstr(7*x[0] + 1*x[2] <= 71)
    m.addConstr(7*x[0] + 13*x[3] <= 106)
    m.addConstr(4*x[1] + 13*x[3] <= 29)
    m.addConstr(7*x[0] + 4*x[1] + 1*x[2] + 13*x[3] <= 29)


    # Optimize model
    m.optimize()

    if m.status == gp.GRB.OPTIMAL:
        for v in m.getVars():
            print('%s %g' % (v.varName, v.x))
        print('Obj: %g' % m.objVal)
    elif m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print(f"Optimization ended with status {m.status}")


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

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