## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'hours worked by Hank' and 'hours worked by Jean'. Let's denote 'hours worked by Hank' as $x_1$ and 'hours worked by Jean' as $x_2$. The objective function to minimize is $8.95x_1 + 2.52x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $21x_1 \leq 148$ (organization score for Hank, but this seems to be an upper bound per hour, so it's actually $21x_1 \leq 148$)
- $30x_1 \leq 303$ (likelihood to quit index for Hank)
- $27x_1 \leq 228$ (paperwork competence rating for Hank)
- $13x_2 \leq 148$ (organization score for Jean)
- $4x_2 \leq 303$ (likelihood to quit index for Jean)
- $28x_2 \leq 228$ (paperwork competence rating for Jean)
- $21x_1 + 13x_2 \geq 69$ (total combined organization score)
- $30x_1 + 4x_2 \geq 111$ (total combined likelihood to quit index)
- $27x_1 + 28x_2 \geq 51$ (total combined paperwork competence rating)
- $-5x_1 + 4x_2 \geq 0$
- $21x_1 + 13x_2 \leq 125$ (upper bound for total combined organization score)
- $30x_1 + 4x_2 \leq 274$ (upper bound for total combined likelihood to quit index)
- $27x_1 + 28x_2 \leq 168$ (upper bound for total combined paperwork competence rating)

## 3: Correct and simplify the constraints
Upon review, the initial interpretation seems mostly correct, but let's ensure accuracy:
- The upper bounds per hour for each resource are already considered in the problem statement.
- The actual constraints from the problem statement regarding individual scores are already given and should be directly translated.

## 4: Formulate the symbolic representation
The symbolic variables are:
- $x_1$: hours worked by Hank
- $x_2$: hours worked by Jean

The objective function is: $8.95x_1 + 2.52x_2$

The constraints are:
1. $21x_1 \leq 148$
2. $30x_1 \leq 303$
3. $27x_1 \leq 228$
4. $13x_2 \leq 148$
5. $4x_2 \leq 303$
6. $28x_2 \leq 228$
7. $21x_1 + 13x_2 \geq 69$
8. $30x_1 + 4x_2 \geq 111$
9. $27x_1 + 28x_2 \geq 51$
10. $-5x_1 + 4x_2 \geq 0$
11. $21x_1 + 13x_2 \leq 125$
12. $30x_1 + 4x_2 \leq 274$
13. $27x_1 + 28x_2 \leq 168$

## 5: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'hours worked by Hank'), ('x2', 'hours worked by Jean')],
    'objective_function': '8.95*x1 + 2.52*x2',
    'constraints': [
        '21*x1 <= 148',
        '30*x1 <= 303',
        '27*x1 <= 228',
        '13*x2 <= 148',
        '4*x2 <= 303',
        '28*x2 <= 228',
        '21*x1 + 13*x2 >= 69',
        '30*x1 + 4*x2 >= 111',
        '27*x1 + 28*x2 >= 51',
        '-5*x1 + 4*x2 >= 0',
        '21*x1 + 13*x2 <= 125',
        '30*x1 + 4*x2 <= 274',
        '27*x1 + 28*x2 <= 168'
    ]
}
```

## 6: Write the Gurobi code
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # hours worked by Hank
    x2 = model.addVar(name="x2", lb=0)  # hours worked by Jean

    # Objective function
    model.setObjective(8.95 * x1 + 2.52 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(21 * x1 <= 148)
    model.addConstr(30 * x1 <= 303)
    model.addConstr(27 * x1 <= 228)
    model.addConstr(13 * x2 <= 148)
    model.addConstr(4 * x2 <= 303)
    model.addConstr(28 * x2 <= 228)
    model.addConstr(21 * x1 + 13 * x2 >= 69)
    model.addConstr(30 * x1 + 4 * x2 >= 111)
    model.addConstr(27 * x1 + 28 * x2 >= 51)
    model.addConstr(-5 * x1 + 4 * x2 >= 0)
    model.addConstr(21 * x1 + 13 * x2 <= 125)
    model.addConstr(30 * x1 + 4 * x2 <= 274)
    model.addConstr(27 * x1 + 28 * x2 <= 168)

    # Optimize
    model.optimize()

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

solve_optimization_problem()
```