To solve the optimization problem described, we will use the Gurobi Python interface to model and solve the problem. The goal is to maximize the objective function `7.94 * hours_worked_by_Bill + 3.2 * hours_worked_by_Jean` subject to several constraints involving the dollar cost per hour for Bill and Jean, and their combined work hours.

First, let's break down the given information:
- Objective: Maximize `7.94 * hours_worked_by_Bill + 3.2 * hours_worked_by_Jean`.
- Constraints:
  - The total combined dollar cost per hour from hours worked by Bill plus hours worked by Jean should be no less than 15.
  - `-6 * hours_worked_by_Bill + 3 * hours_worked_by_Jean >= 0`.
  - The total combined dollar cost per hour from hours worked by Bill and hours worked by Jean has to be as much or less than 42.
  - Since the upper bound of the total cost is explicitly mentioned as 42, we will ensure that our model accounts for this upper limit correctly.

Given the nature of the problem, let's express the constraints in terms of the variables `hours_worked_by_Bill` and `hours_worked_by_Jean`, keeping in mind their respective costs per hour are $6 and $12.

Let's denote:
- `B` as the hours worked by Bill,
- `J` as the hours worked by Jean.

The constraints can be formulated as follows:
1. Total combined dollar cost constraint: `6*B + 12*J >= 15`.
2. Linear constraint: `-6*B + 3*J >= 0`.
3. Upper bound constraint on total dollar cost: `6*B + 12*J <= 42`.

Now, let's implement the problem in Gurobi:

```python
from gurobipy import *

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

# Define variables
hours_worked_by_Bill = m.addVar(lb=0, name="hours_worked_by_Bill")
hours_worked_by_Jean = m.addVar(vtype=GRB.INTEGER, lb=0, name="hours_worked_by_Jean")

# Objective function: Maximize 7.94 * hours_worked_by_Bill + 3.2 * hours_worked_by_Jean
m.setObjective(7.94 * hours_worked_by_Bill + 3.2 * hours_worked_by_Jean, GRB.MAXIMIZE)

# Constraints
m.addConstr(6 * hours_worked_by_Bill + 12 * hours_worked_by_Jean >= 15, name="total_cost_lower_bound")
m.addConstr(-6 * hours_worked_by_Bill + 3 * hours_worked_by_Jean >= 0, name="linear_constraint")
m.addConstr(6 * hours_worked_by_Bill + 12 * hours_worked_by_Jean <= 42, name="total_cost_upper_bound")

# Optimize model
m.optimize()

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