```json
{
  "sym_variables": [
    ("x0", "hours worked by Jean"),
    ("x1", "hours worked by Mary"),
    ("x2", "hours worked by Bobby"),
    ("x3", "hours worked by Dale"),
    ("x4", "hours worked by Ringo")
  ],
  "objective_function": "1*x0 + 9*x1 + 3*x2 + 7*x3 + 2*x4",
  "constraints": [
    "18*x1 + 21*x3 + 5*x4 >= 34",
    "3*x0 + 18*x2 + 5*x4 >= 34",
    "3*x0 + 18*x2 + 21*x3 >= 34",
    "3*x0 + 18*x1 + 18*x2 >= 34",
    "3*x0 + 18*x1 + 21*x3 >= 34",
    "18*x1 + 21*x3 + 5*x4 >= 38",
    "3*x0 + 18*x2 + 5*x4 >= 38",
    "3*x0 + 18*x2 + 21*x3 >= 38",
    "3*x0 + 18*x1 + 18*x2 >= 38",
    "3*x0 + 18*x1 + 21*x3 >= 38",
    "18*x1 + 21*x3 + 5*x4 >= 39",
    "3*x0 + 18*x2 + 5*x4 >= 39",
    "3*x0 + 18*x2 + 21*x3 >= 39",
    "3*x0 + 18*x1 + 18*x2 >= 39",
    "3*x0 + 18*x1 + 21*x3 >= 39",
    "18*x1 + 21*x3 + 5*x4 >= 38", 
    "3*x0 + 18*x2 + 5*x4 >= 38",
    "3*x0 + 18*x2 + 21*x3 >= 38",
    "3*x0 + 18*x1 + 18*x2 >= 38",
    "3*x0 + 18*x1 + 21*x3 >= 38",
    "18*x1 + 21*x3 + 5*x4 >= 41",
    "3*x0 + 18*x2 + 5*x4 >= 41",
    "3*x0 + 18*x2 + 21*x3 >= 41",
    "3*x0 + 18*x1 + 18*x2 >= 41",
    "3*x0 + 18*x1 + 21*x3 >= 41",
    "1*x0 + 21*x1 >= 21",
    "2*x3 + 2*x4 >= 15",
    "1*x0 + 2*x4 >= 36",
    "21*x1 + 2*x4 >= 29",
    "21*x1 + 20*x2 >= 15",
    "20*x2 + 2*x3 >= 12",
    "21*x1 + 2*x3 >= 33",
    "1*x0 + 2*x3 >= 17",
    "1*x0 + 21*x1 + 2*x3 >= 33",
    "21*x1 + 2*x3 + 2*x4 >= 33",
    "1*x0 + 21*x1 + 20*x2 >= 33",
    "20*x2 + 2*x3 + 2*x4 >= 33",
    "21*x1 + 20*x2 + 2*x4 >= 33",
    "1*x0 + 21*x1 + 2*x4 >= 33",
    "1*x0 + 20*x2 + 2*x3 >= 33",
    "8*x0 + 13*x1 + 5*x3 >= 42",
    "12*x0 + 14*x1 >= 42",
    "12*x0 + 10*x4 >= 31",
    "21*x2 + 22*x3 >= 27",
    "14*x1 + 22*x3 >= 15",
    "18*x1 + 18*x2 <= 214",
    "18*x2 + 5*x4 <= 70",
    "3*x0 + 18*x1 <= 88",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0",
    "x4 >= 0",
    "x1 == int(x1)",  
    "x3 == int(x3)"
  ]
}
```

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

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

# Create variables
x = m.addVars(5, lb=0.0, vtype=[GRB.CONTINUOUS, GRB.INTEGER, GRB.CONTINUOUS, GRB.INTEGER, GRB.CONTINUOUS], name=["Jean", "Mary", "Bobby", "Dale", "Ringo"])


# Set objective function
m.setObjective(1*x[0] + 9*x[1] + 3*x[2] + 7*x[3] + 2*x[4], GRB.MAXIMIZE)

# Add constraints

# Computer competence rating constraints
m.addConstr(18*x[1] + 21*x[3] + 5*x[4] >= 34)
m.addConstr(3*x[0] + 18*x[2] + 5*x[4] >= 34)
m.addConstr(3*x[0] + 18*x[2] + 21*x[3] >= 34)
m.addConstr(3*x[0] + 18*x[1] + 18*x[2] >= 34)
m.addConstr(3*x[0] + 18*x[1] + 21*x[3] >= 34)


# ... (rest of the constraints from the JSON "constraints" section)
m.addConstr(18*x[1] + 18*x[2] <= 214)
m.addConstr(18*x[2] + 5*x[4] <= 70)
m.addConstr(3*x[0] + 18*x[1] <= 88)



# Optimize model
m.optimize()

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

```