## Problem Description and Formulation

The problem is a linear programming (LP) optimization problem that aims to minimize the cost of consuming elk meat and bison meat while meeting the daily requirements of iron and zinc.

Let's define the variables:
- $x$: the number of servings of elk meat
- $y$: the number of servings of bison meat

The objective function to minimize is the total cost:
\[ \text{Minimize:} \quad 6x + 7y \]

Subject to the constraints:
- Iron requirement: $5x + 4y \geq 30$
- Zinc requirement: $3x + 4y \geq 40$
- Non-negativity constraints: $x \geq 0, y \geq 0$

## Gurobi Code

```python
import gurobi

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

    # Define the variables
    x = model.addVar(lb=0, name="elk_meat")
    y = model.addVar(lb=0, name="bison_meat")

    # Define the objective function
    model.setObjective(6*x + 7*y, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(5*x + 4*y >= 30, name="iron_requirement")
    model.addConstr(3*x + 4*y >= 40, name="zinc_requirement")

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Elk meat servings: {x.varValue}")
        print(f"Bison meat servings: {y.varValue}")
        print(f"Total cost: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model status is not optimal or infeasible.")

# Run the function
solve_optimization_problem()
```