## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of corn
- $x_2$ represents the acres of peas

## Step 2: Translate the objective function into symbolic notation
The profit per acre of corn is $200, and the profit per acre of peas is $250. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 200x_1 + 250x_2 \]

## 3: Translate the constraints into symbolic notation
1. Land constraint: 40 acres of land available
\[ x_1 + x_2 \leq 40 \]
2. Fertilizer constraint: $4350 available, corn requires $50/acre, peas require $60/acre
\[ 50x_1 + 60x_2 \leq 4350 \]
3. Time constraint: 6000 minutes available, corn requires 60 minutes/acre, peas require 90 minutes/acre
\[ 60x_1 + 90x_2 \leq 6000 \]
4. Non-negativity constraints:
\[ x_1 \geq 0, x_2 \geq 0 \]

## 4: Symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'acres of corn'), ('x2', 'acres of peas')],
'objective_function': '200*x1 + 250*x2',
'constraints': [
'x1 + x2 <= 40',
'50*x1 + 60*x2 <= 4350',
'60*x1 + 90*x2 <= 6000',
'x1 >= 0',
'x2 >= 0'
]
}
```

## Step 5: Gurobi code in Python
```python
import gurobipy as gp

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

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

# Objective function: Maximize profit
m.setObjective(200*x1 + 250*x2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 40, name="land_constraint")
m.addConstr(50*x1 + 60*x2 <= 4350, name="fertilizer_constraint")
m.addConstr(60*x1 + 90*x2 <= 6000, name="time_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Acres of corn: {x1.varValue}")
    print(f"Acres of peas: {x2.varValue}")
    print(f"Max profit: {m.objVal}")
else:
    print("No optimal solution found.")
```