To solve Julia's problem of minimizing her daily cost while meeting her iron and biotin requirements, we can formulate this as a linear programming problem. The goal is to find the number of scoops of Gamma and Delta powders that minimize the total cost while ensuring Julia gets at least 60 grams of iron and 45 grams of biotin.

Let's denote:
- \(x\) as the number of scoops of Gamma powder.
- \(y\) as the number of scoops of Delta powder.

The objective function to minimize is the total cost, which can be represented as \(1.5x + 2.5y\), since each scoop of Gamma costs $1.5 and each scoop of Delta costs $2.5.

The constraints are:
1. Iron requirement: \(7x + 12y \geq 60\) (since a scoop of Gamma contains 7 grams of iron and a scoop of Delta contains 12 grams of iron, Julia needs at least 60 grams).
2. Biotin requirement: \(10x + 9y \geq 45\) (since a scoop of Gamma contains 10 grams of biotin and a scoop of Delta contains 9 grams of biotin, Julia needs at least 45 grams).
3. Non-negativity constraints: \(x \geq 0\) and \(y \geq 0\), because Julia cannot buy a negative number of scoops.

Here is the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a new model
m = Model("Julia_Supplement_Optimization")

# Define variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="Gamma_Scoops")
y = m.addVar(vtype=GRB.CONTINUOUS, name="Delta_Scoops")

# Set the 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 the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Gamma Scoops: {x.x}")
    print(f"Delta Scoops: {y.x}")
    print(f"Total Cost: ${1.5*x.x + 2.5*y.x:.2f}")
else:
    print("No optimal solution found")
```