## Problem Description and Formulation

The gardener needs to minimize the cost of purchasing two types of soil, indoor and outdoor, to meet the weekly requirements of at least 80 units of compost and 70 units of loam.

Let's denote:
- \(x\) as the amount of indoor soil to purchase
- \(y\) as the amount of outdoor soil to purchase

The cost of indoor soil is $2 per unit, and the cost of outdoor soil is $3 per unit. Therefore, the total cost \(C\) to minimize is:
\[ C = 2x + 3y \]

The indoor soil contains 2 units of compost and 3 units of loam per unit, while the outdoor soil contains 4 units of compost and 6 units of loam per unit. The gardener requires at least 80 units of compost and 70 units of loam per week. This gives us the following constraints:
\[ 2x + 4y \geq 80 \]
\[ 3x + 6y \geq 70 \]

Also, \(x \geq 0\) and \(y \geq 0\) since the gardener cannot purchase a negative amount of soil.

## Converting to Gurobi Code

We will use the Gurobi Python API to model and solve this linear programming problem.

```python
import gurobi

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

    # Define variables
    x = model.addVar(name="indoor_soil", lb=0)  # Amount of indoor soil
    y = model.addVar(name="outdoor_soil", lb=0)  # Amount of outdoor soil

    # Objective function: Minimize cost
    model.setObjective(2*x + 3*y, gurobi.GRB.MINIMIZE)

    # Constraints: Meet compost and loam requirements
    model.addConstr(2*x + 4*y >= 80, name="compost_requirement")
    model.addConstr(3*x + 6*y >= 70, name="loam_requirement")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal amount of indoor soil: {x.varValue}")
        print(f"Optimal amount of outdoor soil: {y.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found")

solve_soil_mix_problem()
```