To solve Peter's problem, we need to determine the minimum cost of producing a plant growth compound that meets certain requirements by mixing two types of fertilizer: GreenCycle and GrowSafe. The compound must contain at least 9 units of nitrous oxide and 5 units of vitamin mix.

Let's denote:
- \(x_1\) as the amount (in kg) of GreenCycle fertilizer used.
- \(x_2\) as the amount (in kg) of GrowSafe fertilizer used.

The objective is to minimize the total cost, which can be represented as:
\[ \text{Minimize} \quad 1.5x_1 + 1.8x_2 \]

Subject to the constraints:
- The compound must contain at least 9 units of nitrous oxide:
\[ 2.1x_1 + 3.5x_2 \geq 9 \]
- The compound must contain at least 5 units of vitamin mix:
\[ 1.3x_1 + 1.1x_2 \geq 5 \]
- Non-negativity constraints, as the amount of fertilizer cannot be negative:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

This problem can be solved using linear programming techniques and implemented in Gurobi.

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, name="GreenCycle")
x2 = m.addVar(lb=0, name="GrowSafe")

# Set the objective function
m.setObjective(1.5*x1 + 1.8*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(2.1*x1 + 3.5*x2 >= 9, "Nitrous_Oxide")
m.addConstr(1.3*x1 + 1.1*x2 >= 5, "Vitamin_Mix")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Minimum cost: $", m.objVal)
    print("GreenCycle (kg): ", x1.x)
    print("GrowSafe (kg): ", x2.x)
else:
    print("The model is infeasible")
```