To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. The variables are 'hours worked by Laura' and 'hours worked by Bobby', which we can denote as $x_1$ and $x_2$, respectively.

The objective function is to maximize $9x_1 + 4x_2$.

Given constraints:
1. $0.46x_1 + 6.11x_2 \geq 37$ (Total combined computer competence rating should be at least 37)
2. $-x_1 + 4x_2 \geq 0$ (Constraint on hours worked by Laura and Bobby)
3. $0.46x_1 + 6.11x_2 \leq 102$ (Total combined computer competence rating should be less than or equal to 102)

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'hours worked by Laura'), ('x2', 'hours worked by Bobby')],
    'objective_function': '9*x1 + 4*x2',
    'constraints': [
        '0.46*x1 + 6.11*x2 >= 37',
        '-x1 + 4*x2 >= 0',
        '0.46*x1 + 6.11*x2 <= 102'
    ]
}
```

Now, let's implement the solution using Gurobi in Python:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="hours_worked_by_Laura")
x2 = m.addVar(lb=0, name="hours_worked_by_Bobby")

# Set the objective function
m.setObjective(9*x1 + 4*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(0.46*x1 + 6.11*x2 >= 37, "computer_competence_rating_min")
m.addConstr(-x1 + 4*x2 >= 0, "hours_worked_constraint")
m.addConstr(0.46*x1 + 6.11*x2 <= 102, "computer_competence_rating_max")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Laura: {x1.x}")
    print(f"Hours worked by Bobby: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```