## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by John' and 'hours worked by Bobby', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $9x_1 + 6x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- Productivity rating constraint: $1x_1 + 1x_2 \geq 21$
- Computer competence rating constraint: $6x_1 + 9x_2 \geq 24$
- Linear constraint: $-7x_1 + 4x_2 \geq 0$
- Upper bound productivity constraint: $1x_1 + 1x_2 \leq 56$
- Upper bound computer competence constraint: $6x_1 + 9x_2 \leq 42$

## 4: Consider the resource attributes
The resource attributes given are:
- $r_0$: productivity rating with $x_0 = 1$, $x_1 = 1$ for John and Bobby respectively, and an upper bound of 104.
- $r_1$: computer competence rating with $x_0 = 6$, $x_1 = 9$ for John and Bobby respectively, and an upper bound of 85.

## 5: Formulate the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'hours worked by John'), ('x2', 'hours worked by Bobby')],
'objective_function': '9*x1 + 6*x2',
'constraints': [
    '1*x1 + 1*x2 >= 21',
    '6*x1 + 9*x2 >= 24',
    '-7*x1 + 4*x2 >= 0',
    '1*x1 + 1*x2 <= 56',
    '6*x1 + 9*x2 <= 42'
]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="hours_worked_by_John", lb=0)  # Assuming hours cannot be negative
    x2 = model.addVar(name="hours_worked_by_Bobby", lb=0)  # Assuming hours cannot be negative

    # Define the objective function
    model.setObjective(9 * x1 + 6 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 >= 21, name="productivity_rating_constraint")
    model.addConstr(6 * x1 + 9 * x2 >= 24, name="computer_competence_rating_constraint")
    model.addConstr(-7 * x1 + 4 * x2 >= 0, name="linear_constraint")
    model.addConstr(x1 + x2 <= 56, name="upper_bound_productivity_constraint")
    model.addConstr(6 * x1 + 9 * x2 <= 42, name="upper_bound_computer_competence_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Hours worked by John: {x1.varValue}")
        print(f"Hours worked by Bobby: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```