## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the amount of GreenCycle fertilizer in kg.
- $x_2$ represents the amount of GrowSafe fertilizer in kg.

## Step 2: Formulate the objective function
The objective is to minimize the cost of the compound. The cost of GreenCycle is $1.5 per kg, and the cost of GrowSafe is $1.8 per kg. Therefore, the objective function can be represented as:
\[ \text{Minimize:} \quad 1.5x_1 + 1.8x_2 \]

## 3: Define the constraints
The compound must contain at least 9 units of nitrous oxide and 5 units of vitamin mix.
- GreenCycle contains 2.1 units of nitrous oxide per kg and 1.3 units of vitamin mix per kg.
- GrowSafe contains 3.5 units of nitrous oxide per kg and 1.1 units of vitamin mix per kg.
Therefore, the constraints can be represented as:
\[ 2.1x_1 + 3.5x_2 \geq 9 \]
\[ 1.3x_1 + 1.1x_2 \geq 5 \]
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the amount of fertilizer cannot be negative.

## 4: Symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'GreenCycle'), ('x2', 'GrowSafe')],
'objective_function': '1.5*x1 + 1.8*x2',
'constraints': [
    '2.1*x1 + 3.5*x2 >= 9',
    '1.3*x1 + 1.1*x2 >= 5',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Gurobi code
Now, let's write the Gurobi code in Python to solve this problem:
```python
import gurobipy as gp

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

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

# Define the objective function
model.setObjective(1.5 * x1 + 1.8 * x2, gp.GRB.MINIMIZE)

# Add constraints
model.addConstr(2.1 * x1 + 3.5 * x2 >= 9, name="nitrous_oxide")
model.addConstr(1.3 * x1 + 1.1 * x2 >= 5, name="vitamin_mix")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"GreenCycle: {x1.varValue} kg")
    print(f"GrowSafe: {x2.varValue} kg")
    print(f"Minimum cost: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```