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

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $6.49x_1 + 9.41x_2$.

## Step 3: List the constraints
The constraints are:
- $8x_1 \geq 8$ (Laura's paperwork competence rating is 8, but this seems to be an equality, and it should be considered as $8x_1 = 8$ or simply $x_1 \geq 1$ if we consider $x_1$ as a fraction of the maximum rating, however, based on the attribute given, it seems we should directly use $8x_1 = 8$)
- $11x_1 \geq 11$ (Laura's organization score is 11, similarly, $11x_1 = 11$)
- $7x_1 \geq 7$ (Laura's productivity rating is 7, $7x_1 = 7$)
- $4x_2 \geq 4$ (Ringo's paperwork competence rating is 4, $4x_2 = 4$)
- $7x_2 \geq 7$ (Ringo's organization score is 7, $7x_2 = 7$)
- $2x_2 \geq 2$ (Ringo's productivity rating is 2, $2x_2 = 2$)
- $8x_1 + 4x_2 \geq 36$
- $11x_1 + 7x_2 \geq 44$
- $7x_1 + 2x_2 \geq 28$
- $10x_1 - 5x_2 \geq 0$
- $8x_1 + 4x_2 \leq 60$
- $11x_1 + 7x_2 \leq 100$
- $7x_1 + 2x_2 \leq 54$

## 4: Correcting and Simplifying Constraints
Upon closer inspection, the constraints for individual ratings seem to be equalities rather than inequalities since they describe specific scores:
- $8x_1 = 8$
- $11x_1 = 11$
- $7x_1 = 7$
- $4x_2 = 4$
- $7x_2 = 7$
- $2x_2 = 2$

These simplify to:
- $x_1 = 1$
- $x_2 = 1$

## 5: Formulate the problem in Gurobi
Given the corrections, we realize that $x_1 = 1$ and $x_2 = 1$ directly from the individual ratings, which makes the problem simpler but we need to ensure these satisfy all other constraints.

## 6: Checking if the direct values satisfy all constraints
- $8*1 + 4*1 = 12 \geq 36$ is false.
This indicates a potential issue with directly applying the equality constraints without considering the bounds and other constraints.

## 7: Re-evaluate the Approach
Given that $x_1$ and $x_2$ represent hours worked and must be non-negative, and considering the constraints:
We should directly formulate the problem with Gurobi.

## 8: Gurobi Formulation
```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

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

# Objective function
m.setObjective(6.49*x1 + 9.41*x2, gp.GRB.MINIMIZE)

# Constraints
m.addConstr(8*x1 + 4*x2 >= 36, name="paperwork_competence")
m.addConstr(11*x1 + 7*x2 >= 44, name="organization_score")
m.addConstr(7*x1 + 2*x2 >= 28, name="productivity_rating")
m.addConstr(10*x1 - 5*x2 >= 0, name="combined_rating_constraint")
m.addConstr(8*x1 + 4*x2 <= 60, name="paperwork_competence_upper_bound")
m.addConstr(11*x1 + 7*x2 <= 100, name="organization_score_upper_bound")
m.addConstr(7*x1 + 2*x2 <= 54, name="productivity_rating_upper_bound")

# Solve the problem
m.optimize()

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

## 9: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'hours worked by Laura'), ('x2', 'hours worked by Ringo')],
    'objective_function': '6.49*x1 + 9.41*x2',
    'constraints': [
        '8*x1 + 4*x2 >= 36',
        '11*x1 + 7*x2 >= 44',
        '7*x1 + 2*x2 >= 28',
        '10*x1 - 5*x2 >= 0',
        '8*x1 + 4*x2 <= 60',
        '11*x1 + 7*x2 <= 100',
        '7*x1 + 2*x2 <= 54'
    ]
}
```