## Problem Description and Formulation

The problem is a linear programming optimization problem. Julia needs to minimize the cost of purchasing two powders, Gamma and Delta, to meet her daily requirements of iron and biotin.

Let's define the decision variables:
- $x$: the number of scoops of Gamma powder Julia buys
- $y$: the number of scoops of Delta powder Julia buys

The objective function is to minimize the total cost:
\[ \text{Minimize:} \quad 1.5x + 2.5y \]

Subject to the constraints:
- Iron requirement: $7x + 12y \geq 60$
- Biotin requirement: $10x + 9y \geq 45$
- Non-negativity constraints: $x \geq 0, y \geq 0$

## Gurobi Code

```python
import gurobi

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

    # Define the decision variables
    x = model.addVar(name="Gamma", lb=0, ub=float('inf'), obj=1.5)
    y = model.addVar(name="Delta", lb=0, ub=float('inf'), obj=2.5)

    # Iron requirement constraint
    model.addConstr(7 * x + 12 * y >= 60, name="Iron_Requirement")

    # Biotin requirement constraint
    model.addConstr(10 * x + 9 * y >= 45, name="Biotin_Requirement")

    # Set the model objective to minimize
    model.setObjective(1.5 * x + 2.5 * y, gurobi.GRB.MINIMIZE)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Gamma (x): {x.varValue}")
        print(f"Delta (y): {y.varValue}")
        print(f"Total Cost: {model.objVal}")
    else:
        print("The model is infeasible.")

julia_supplement_problem()
```