```json
{
  "sym_variables": [
    ("x0", "hours worked by Bill"),
    ("x1", "hours worked by Jean")
  ],
  "objective_function": "1.22*x0 + 7.59*x1",
  "constraints": [
    "12.44*x0 + 6.97*x1 >= 23",
    "1.12*x0 + 0.5*x1 >= 59",
    "10.33*x0 + 9.44*x1 >= 36",
    "1*x0 + -5*x1 >= 0",
    "12.44*x0 + 6.97*x1 <= 53",
    "1.12*x0 + 0.5*x1 <= 90",
    "10.33*x0 + 9.44*x1 <= 96"
  ]
}
```

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

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

    # Create variables
    x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="hours worked by Bill")
    x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="hours worked by Jean")


    # Set objective function
    m.setObjective(1.22 * x0 + 7.59 * x1, GRB.MAXIMIZE)

    # Add constraints
    m.addConstr(12.44 * x0 + 6.97 * x1 >= 23, "computer_competence_min")
    m.addConstr(1.12 * x0 + 0.5 * x1 >= 59, "likelihood_to_quit_min")
    m.addConstr(10.33 * x0 + 9.44 * x1 >= 36, "work_quality_min")
    m.addConstr(1 * x0 - 5 * x1 >= 0, "bill_jean_relation")
    m.addConstr(12.44 * x0 + 6.97 * x1 <= 53, "computer_competence_max")
    m.addConstr(1.12 * x0 + 0.5 * x1 <= 90, "likelihood_to_quit_max")
    m.addConstr(10.33 * x0 + 9.44 * x1 <= 96, "work_quality_max")



    # Optimize model
    m.optimize()

    if m.status == GRB.OPTIMAL:
        print('Obj: %g' % m.objVal)
        print('x0: %g' % x0.x)
        print('x1: %g' % x1.x)
    elif m.status == GRB.INFEASIBLE:
        print('The 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')
```