To solve this optimization problem, we will first translate the given natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using algebraic terms.

Let's define:
- $x_1$ as the hours worked by Paul,
- $x_2$ as the hours worked by Peggy.

The objective function to maximize is: $7.41x_1 + 3.98x_2$

Given the resources/attributes and constraints, we have:
- The total combined dollar cost per hour from hours worked by Paul plus hours worked by Peggy must be at least 34.
- The total combined productivity rating from hours worked by Paul and hours worked by Peggy has to be at least 31.
- $-9x_1 + 3x_2 \geq 0$
- The total combined dollar cost per hour from hours worked by Paul plus hours worked by Peggy should be at most 75.
- The total combined productivity rating from hours worked by Paul and hours worked by Peggy must be at maximum 38.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'hours worked by Paul'), ('x2', 'hours worked by Peggy')],
    'objective_function': '7.41*x1 + 3.98*x2',
    'constraints': [
        '7*x1 + 6*x2 >= 34',  # Total combined dollar cost per hour at least 34
        '6*x1 + 5*x2 >= 31',  # Total combined productivity rating at least 31
        '-9*x1 + 3*x2 >= 0',  # Constraint on hours worked by Paul and Peggy
        '7*x1 + 6*x2 <= 75',  # Total combined dollar cost per hour at most 75
        '6*x1 + 5*x2 <= 38'   # Total combined productivity rating at most 38
    ]
}
```

Now, let's implement this optimization problem in Gurobi:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="hours_worked_by_Paul")
x2 = m.addVar(lb=0, name="hours_worked_by_Peggy")

# Set the objective function
m.setObjective(7.41*x1 + 3.98*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 6*x2 >= 34, "total_dollar_cost_at_least_34")
m.addConstr(6*x1 + 5*x2 >= 31, "total_productivity_rating_at_least_31")
m.addConstr(-9*x1 + 3*x2 >= 0, "constraint_on_hours_worked")
m.addConstr(7*x1 + 6*x2 <= 75, "total_dollar_cost_at_most_75")
m.addConstr(6*x1 + 5*x2 <= 38, "total_productivity_rating_at_most_38")

# Optimize model
m.optimize()

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