Here's how we can formulate this problem and translate it into Gurobi code:

**Decision Variables:**

* `x`: Number of scoops of Gamma
* `y`: Number of scoops of Delta

**Objective Function:**

Minimize the total cost:

```
Minimize: 1.5x + 2.5y
```

**Constraints:**

* **Iron:**  7x + 12y >= 60  (Julia needs at least 60 grams of iron)
* **Biotin:** 10x + 9y >= 45   (Julia needs at least 45 grams of biotin)
* **Non-negativity:** x, y >= 0 (Julia cannot take negative scoops)


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("SupplementMix")

# Create variables
x = m.addVar(lb=0, name="Gamma_Scoops")  # Scoops of Gamma
y = m.addVar(lb=0, name="Delta_Scoops")  # Scoops of Delta

# Set objective function
m.setObjective(1.5 * x + 2.5 * y, GRB.MINIMIZE)

# Add constraints
m.addConstr(7 * x + 12 * y >= 60, "Iron_Requirement")
m.addConstr(10 * x + 9 * y >= 45, "Biotin_Requirement")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal Cost: ${m.objVal:.2f}")
    print(f"Gamma Scoops: {x.x:.2f}")
    print(f"Delta Scoops: {y.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {m.status}")

```
