To solve this optimization problem, we need to first identify the decision variables, objective function, and constraints.

The decision variables are:
- `x0`: hours worked by Laura
- `x1`: hours worked by Dale

The objective function is to maximize: `6.13*x0 + 3.02*x1`

There are several constraints given in the problem statement. We will translate each of them into a mathematical expression and then implement these in Gurobi.

Constraints:
1. Total combined productivity rating ≥ 41: `4.87*x0 + 1.72*x1 ≥ 41`
2. Total combined organization score ≥ 18: `0.28*x0 + 6.84*x1 ≥ 18`
3. Total combined paperwork competence rating ≥ 17: `6.33*x0 + 4.33*x1 ≥ 17`
4. Total combined computer competence rating ≥ 110: `5.0*x0 + 3.52*x1 ≥ 110`
5. `-x0 + 6*x1 ≥ 0`
6. Total combined productivity rating ≤ 92: `4.87*x0 + 1.72*x1 ≤ 92`
7. Total combined organization score ≤ 58: `0.28*x0 + 6.84*x1 ≤ 58`
8. Total combined paperwork competence rating ≤ 37: `6.33*x0 + 4.33*x1 ≤ 37`
9. Total combined computer competence rating ≤ 231: `5.0*x0 + 3.52*x1 ≤ 231`

Given that both `x0` and `x1` can be non-integer (i.e., they are continuous variables), we will use Gurobi's LP (Linear Programming) solver to find the optimal solution.

Here is how you could write this optimization problem in Python using Gurobi:

```python
from gurobipy import *

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

# Define decision variables
x0 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Laura")
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="hours_worked_by_Dale")

# Objective function: Maximize 6.13*x0 + 3.02*x1
m.setObjective(6.13*x0 + 3.02*x1, GRB.MAXIMIZE)

# Constraints
m.addConstr(4.87*x0 + 1.72*x1 >= 41, name="productivity_rating_min")
m.addConstr(0.28*x0 + 6.84*x1 >= 18, name="organization_score_min")
m.addConstr(6.33*x0 + 4.33*x1 >= 17, name="paperwork_competence_rating_min")
m.addConstr(5.0*x0 + 3.52*x1 >= 110, name="computer_competence_rating_min")
m.addConstr(-x0 + 6*x1 >= 0, name="mixed_constraint_1")

m.addConstr(4.87*x0 + 1.72*x1 <= 92, name="productivity_rating_max")
m.addConstr(0.28*x0 + 6.84*x1 <= 58, name="organization_score_max")
m.addConstr(6.33*x0 + 4.33*x1 <= 37, name="paperwork_competence_rating_max")
m.addConstr(5.0*x0 + 3.52*x1 <= 231, name="computer_competence_rating_max")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hours worked by Laura: {x0.x}")
    print(f"Hours worked by Dale: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```