## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:
- $x_1$ represents the acres of land for spinach
- $x_2$ represents the acres of land for kale

The objective is to maximize profit. Given that the profit per acre of spinach is $20 and the profit per acre of kale is $30, the objective function can be represented as:
\[ \text{Maximize:} \quad 20x_1 + 30x_2 \]

The constraints based on the given information are:
1. Total land constraint: $x_1 + x_2 \leq 300$
2. Budget constraint for seeds: $40x_1 + 50x_2 \leq 14000$
3. Maintenance hours constraint: $x_1 + 2x_2 \leq 450$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'acres of spinach'), ('x2', 'acres of kale')],
    'objective_function': '20*x1 + 30*x2',
    'constraints': [
        'x1 + x2 <= 300',
        '40*x1 + 50*x2 <= 14000',
        'x1 + 2*x2 <= 450',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Linda's Farm")

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

# Objective function: Maximize profit
model.setObjective(20*x1 + 30*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(x1 + x2 <= 300, name="land_constraint")
model.addConstr(40*x1 + 50*x2 <= 14000, name="budget_constraint")
model.addConstr(x1 + 2*x2 <= 450, name="maintenance_constraint")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal acres of spinach: {x1.varValue}")
    print(f"Optimal acres of kale: {x2.varValue}")
    print(f"Maximal profit: {model.objVal}")
else:
    print("The model is infeasible.")
```