## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'chives' and 'basil plants', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $7x_1 + 7x_2$. The constraints can be represented as follows:
- $0.15x_1 + 0.99x_2 \geq 33$ (water need)
- $0.95x_1 + 0.06x_2 \geq 50$ (beauty rating)
- $0.17x_1 + 0.6x_2 \geq 27$ (planting space)
- $0.44x_1 + 0.75x_2 \geq 25$ (resilience index)
- $5x_1 - 9x_2 \geq 0$ (additional constraint)
- $0.15x_1 + 0.99x_2 \leq 112$ (maximum water available)
- $0.95x_1 + 0.06x_2 \leq 77$ (maximum beauty rating)
- $0.17x_1 + 0.6x_2 \leq 123$ (maximum planting space)
- $0.44x_1 + 0.75x_2 \leq 105$ (maximum resilience index)
- $x_1, x_2 \geq 0$ and are integers.

## Step 2: Convert the problem into a Gurobi-compatible format
We need to use Gurobi's Python API to model and solve this problem. The variables are $x_1$ (chives) and $x_2$ (basil plants).

## 3: Write the Gurobi code
```python
import gurobi as gp

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

# Define the variables
x1 = m.addVar(name="chives", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="basil_plants", vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(7*x1 + 7*x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(0.15*x1 + 0.99*x2 >= 33, name="water_need")
m.addConstr(0.95*x1 + 0.06*x2 >= 50, name="beauty_rating")
m.addConstr(0.17*x1 + 0.6*x2 >= 27, name="planting_space")
m.addConstr(0.44*x1 + 0.75*x2 >= 25, name="resilience_index")
m.addConstr(5*x1 - 9*x2 >= 0, name="additional_constraint")
m.addConstr(0.15*x1 + 0.99*x2 <= 112, name="max_water")
m.addConstr(0.95*x1 + 0.06*x2 <= 77, name="max_beauty")
m.addConstr(0.17*x1 + 0.6*x2 <= 123, name="max_planting_space")
m.addConstr(0.44*x1 + 0.75*x2 <= 105, name="max_resilience")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Chives: {x1.varValue}")
    print(f"Basil Plants: {x2.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```

## 4: Symbolic Representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'chives'), ('x2', 'basil plants')],
    'objective_function': '7*x1 + 7*x2',
    'constraints': [
        '0.15*x1 + 0.99*x2 >= 33',
        '0.95*x1 + 0.06*x2 >= 50',
        '0.17*x1 + 0.6*x2 >= 27',
        '0.44*x1 + 0.75*x2 >= 25',
        '5*x1 - 9*x2 >= 0',
        '0.15*x1 + 0.99*x2 <= 112',
        '0.95*x1 + 0.06*x2 <= 77',
        '0.17*x1 + 0.6*x2 <= 123',
        '0.44*x1 + 0.75*x2 <= 105'
    ]
}
```