To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints in terms of these variables.

Let's define the variables as follows:
- $x_1$: hours worked by Paul
- $x_2$: hours worked by Jean
- $x_3$: hours worked by Ringo

The objective function to maximize is: $7.29x_1 + 8.85x_2 + 1.49x_3$

Given constraints:
1. Productivity ratings are given but not directly used as constraints; instead, they're part of the constraints involving total productivity.
2. Total combined productivity rating from hours worked by Paul and Ringo: $7x_1 + 12x_3 \leq 43$
3. Total combined productivity rating from hours worked by Jean and Ringo: $9x_2 + 12x_3 \leq 37$
4. Total combined productivity rating from all three: $7x_1 + 9x_2 + 12x_3 \leq 93$
5. The total combined productivity rating constraint is repeated, implying the same constraint.
6. $x_1$ must be an integer (whole number of hours worked by Paul).
7. $x_2$ must be an integer (integer number of hours worked by Jean).
8. $x_3$ can be a non-integer (non-integer amount of hours worked by Ringo).

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'hours worked by Paul'), ('x2', 'hours worked by Jean'), ('x3', 'hours worked by Ringo')],
  'objective_function': '7.29*x1 + 8.85*x2 + 1.49*x3',
  'constraints': [
    '7*x1 + 12*x3 <= 43',
    '9*x2 + 12*x3 <= 37',
    '7*x1 + 9*x2 + 12*x3 <= 93'
  ]
}
```

Now, let's write the Gurobi code to solve this optimization problem:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="hours_worked_by_Paul")
x2 = m.addVar(vtype=GRB.INTEGER, name="hours_worked_by_Jean")
x3 = m.addVar(vtype=GRB.CONTINUOUS, name="hours_worked_by_Ringo")

# Set the objective function
m.setObjective(7.29*x1 + 8.85*x2 + 1.49*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 12*x3 <= 43, name="productivity_constraint_1")
m.addConstr(9*x2 + 12*x3 <= 37, name="productivity_constraint_2")
m.addConstr(7*x1 + 9*x2 + 12*x3 <= 93, name="total_productivity_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Paul: {x1.x}")
    print(f"Hours worked by Jean: {x2.x}")
    print(f"Hours worked by Ringo: {x3.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```