## Step 1: Define the symbolic representation of the variables
The variables are 'hours worked by John' and 'hours worked by Mary', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the symbolic representation of the resources/attributes
The resources/attributes are given as:
- $r_0$: paperwork competence rating with $x_0 = 5$ for John and $x_1 = 8$ for Mary, and an upper bound of 51.
- $r_1$: organization score with $x_0 = 2$ for John and $x_1 = 8$ for Mary, and an upper bound of 53.

## 3: Formulate the objective function
The objective function to minimize is $9x_1 + 2x_2$.

## 4: Formulate the constraints
The constraints are:
1. $5x_1 \geq 20$ (total combined paperwork competence rating must be at least 20, but since $x_0$ values are given, this translates to $5x_1 + 8x_2 \geq 20$),
2. $2x_1 + 8x_2 \geq 14$ (total combined organization score must be 14 or more),
3. $5x_1 - 4x_2 \geq 0$,
4. $5x_1 + 8x_2 \leq 45$ (total combined paperwork competence rating must be at most 45),
5. $2x_1 + 8x_2 \leq 45$ (total combined organization score must be at most 45).

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'hours worked by John'), ('x2', 'hours worked by Mary')],
'objective_function': '9*x1 + 2*x2',
'constraints': [
    '5*x1 + 8*x2 >= 20',
    '2*x1 + 8*x2 >= 14',
    '5*x1 - 4*x2 >= 0',
    '5*x1 + 8*x2 <= 45',
    '2*x1 + 8*x2 <= 45'
]
}
```

## 6: Gurobi Code
Now, let's implement this in Gurobi:
```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_Mary", lb=0)  # Assuming hours cannot be negative

    # Define the objective function
    model.setObjective(9 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(5 * x1 + 8 * x2 >= 20, name="paperwork_competence_rating_min")
    model.addConstr(2 * x1 + 8 * x2 >= 14, name="organization_score_min")
    model.addConstr(5 * x1 - 4 * x2 >= 0, name="hourly_constraint")
    model.addConstr(5 * x1 + 8 * x2 <= 45, name="paperwork_competence_rating_max")
    model.addConstr(2 * x1 + 8 * x2 <= 45, name="organization_score_max")

    # 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 Mary: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```