## Problem Description and Formulation

The problem requires maximizing the objective function:

\[ 4 \times (\text{hours worked by Dale})^2 + 6 \times (\text{hours worked by Dale}) \times (\text{hours worked by Hank}) + 7 \times (\text{hours worked by Dale}) \]

subject to several constraints:

1. Dale's computer competence rating is 4.
2. Hank's computer competence rating is 4.
3. The total combined computer competence rating from hours worked by Dale plus hours worked by Hank should be at least 29.
4. \(-8 \times (\text{hours worked by Dale}) + 6 \times (\text{hours worked by Hank}) \geq 0\).
5. The total combined computer competence rating from hours worked by Dale plus hours worked by Hank should be at most 70.

## Gurobi Code Formulation

Given the problem description, we can formulate the Gurobi code as follows:

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    dale_hours = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="dale_hours")
    hank_hours = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="hank_hours")

    # Objective function
    model.setObjective(4 * dale_hours ** 2 + 6 * dale_hours * hank_hours + 7 * dale_hours, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4 * dale_hours + 4 * hank_hours >= 29, name="min_computer_competence")
    model.addConstr(-8 * dale_hours + 6 * hank_hours >= 0, name="linear_constraint")
    model.addConstr(4 * dale_hours + 4 * hank_hours <= 70, name="max_computer_competence")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Hours worked by Dale: {dale_hours.varValue}")
        print(f"Hours worked by Hank: {hank_hours.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```

## Note

The code above directly translates the given problem into a Gurobi model. Note that the variables `dale_hours` and `hank_hours` are defined without explicit bounds, as the problem statement does not specify that they must be non-negative or within a certain range, except for the implicit bounds imposed by the constraints. The model is then solved, and the solution (if optimal) or a status message is printed. 

Also, note that Gurobi does not directly support nonlinear constraints or objective functions in all its interfaces, but it does support quadratic objective functions as in this case. 

Please ensure you have Gurobi installed and properly configured in your Python environment to run this code.