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 John' and 'hours worked by Bobby', which we can denote as $x_1$ and $x_2$, respectively.

The objective function is described as minimizing $9x_1 + 6x_2$.

The constraints given in the problem description can be translated into symbolic form as follows:
- Productivity rating of John: $x_1$
- Computer competence rating of John: $6x_1$
- Productivity rating of Bobby: $x_2$
- Computer competence rating of Bobby: $9x_2$
- Total combined productivity rating $\geq 21$: $x_1 + x_2 \geq 21$
- Total combined computer competence rating $\geq 24$: $6x_1 + 9x_2 \geq 24$
- Another constraint given is $-7x_1 + 4x_2 \geq 0$
- The total combined productivity rating $\leq 56$: $x_1 + x_2 \leq 56$
- The total combined computer competence rating $\leq 42$: $6x_1 + 9x_2 \leq 42$

Given these translations, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'hours worked by John'), ('x2', 'hours worked by Bobby')],
    'objective_function': '9*x1 + 6*x2',
    'constraints': [
        'x1 + x2 >= 21',
        '6*x1 + 9*x2 >= 24',
        '-7*x1 + 4*x2 >= 0',
        'x1 + x2 <= 56',
        '6*x1 + 9*x2 <= 42'
    ]
}
```

Now, to solve this problem using Gurobi, we can write the following Python code:

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_John")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Bobby")

# Set the objective function
m.setObjective(9*x1 + 6*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1 + x2 >= 21, "productivity_rating")
m.addConstr(6*x1 + 9*x2 >= 24, "computer_competence_rating_min")
m.addConstr(-7*x1 + 4*x2 >= 0, "other_constraint")
m.addConstr(x1 + x2 <= 56, "productivity_rating_max")
m.addConstr(6*x1 + 9*x2 <= 42, "computer_competence_rating_max")

# Optimize the model
m.optimize()

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