## Problem Description and Formulation

The problem is a classic example of a linear programming problem. Jamie wants to minimize the cost of her diet while meeting the minimum nutritional requirements of 100 units of proteins and 60 units of fat. She can achieve this by consuming a mixture of chicken and beef.

Let's define the decision variables:

* `x`: the number of units of chicken Jamie consumes
* `y`: the number of units of beef Jamie consumes

The objective function is to minimize the total cost:

* `Cost = 3.4x + 7.5y`

The constraints are:

* `10x + 30y >= 100` (minimum protein requirement)
* `6x + 40y >= 60` (minimum fat requirement)
* `x >= 0` and `y >= 0` (non-negativity constraints, as Jamie cannot consume a negative amount of chicken or beef)

## Gurobi Code

```python
import gurobi

# Create a new Gurobi model
model = gurobi.Model()

# Define the decision variables
x = model.addVar(name="chicken", lb=0, ub=gurobi.GRB.INFINITY)
y = model.addVar(name="beef", lb=0, ub=gurobi.GRB.INFINITY)

# Define the objective function
model.setObjective(3.4 * x + 7.5 * y, gurobi.GRB.MINIMIZE)

# Define the constraints
model.addConstr(10 * x + 30 * y >= 100, name="protein_requirement")
model.addConstr(6 * x + 40 * y >= 60, name="fat_requirement")

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Chicken: {x.varValue} units")
    print(f"Beef: {y.varValue} units")
    print(f"Minimum cost: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```