To solve the optimization problem described, we need to first translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Let's define:
- $x_0$ as the hours worked by Bill,
- $x_1$ as the hours worked by Jean.

The objective function is to maximize: $1.22x_0 + 7.59x_1$

Given the attributes and their implications on the constraints:

- Computer competence rating for Bill: $12.44x_0$
- Likelihood to quit index for Bill: $1.12x_0$
- Work quality rating for Bill: $10.33x_0$
- Computer competence rating for Jean: $6.97x_1$
- Likelihood to quit index for Jean: $0.5x_1$
- Work quality rating for Jean: $9.44x_1$

The constraints are:
1. Total combined computer competence rating $\geq 23$: $12.44x_0 + 6.97x_1 \geq 23$
2. Total combined likelihood to quit index $\geq 59$: $1.12x_0 + 0.5x_1 \geq 59$
3. Total combined work quality rating $\geq 36$: $10.33x_0 + 9.44x_1 \geq 36$
4. $x_0 - 5x_1 \geq 0$
5. Total combined computer competence rating $\leq 53$: $12.44x_0 + 6.97x_1 \leq 53$
6. Total combined likelihood to quit index $\leq 90$: $1.12x_0 + 0.5x_1 \leq 90$
7. Total combined work quality rating $\leq 96$: $10.33x_0 + 9.44x_1 \leq 96$

Symbolic representation:
```json
{
    'sym_variables': [('x0', 'hours worked by Bill'), ('x1', 'hours worked by Jean')],
    'objective_function': 'maximize 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',
        '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'
    ]
}
```

Now, let's write the Gurobi code for this optimization problem:

```python
from gurobipy import *

# Create a new model
model = Model("Optimization_Problem")

# Add variables
x0 = model.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Bill")
x1 = model.addVar(vtype=GRB.INTEGER, name="hours_worked_by_Jean")

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

# Add constraints
model.addConstr(12.44*x0 + 6.97*x1 >= 23, "computer_competence_rating")
model.addConstr(1.12*x0 + 0.5*x1 >= 59, "likelihood_to_quit_index_min")
model.addConstr(10.33*x0 + 9.44*x1 >= 36, "work_quality_rating_min")
model.addConstr(x0 - 5*x1 >= 0, "hours_worked_constraint")
model.addConstr(12.44*x0 + 6.97*x1 <= 53, "computer_competence_rating_max")
model.addConstr(1.12*x0 + 0.5*x1 <= 90, "likelihood_to_quit_index_max")
model.addConstr(10.33*x0 + 9.44*x1 <= 96, "work_quality_rating_max")

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Bill: {x0.x}")
    print(f"Hours worked by Jean: {x1.x}")
    print(f"Objective function value: {model.objVal}")
else:
    print("No optimal solution found")
```