To solve the optimization problem described, we first need to formulate it as a linear programming (LP) problem. The goal is to maximize profit given the constraints on water, crab meat, and lobster meat.

Let's define the symbolic variables:
- $x_1$ represents the number of servings of crab soup.
- $x_2$ represents the number of servings of lobster soup.

The objective function aims to maximize profit. Given that the profit per serving of crab soup is $3 and the profit per serving of lobster soup is $5, the objective function can be written as:
\[ \text{Maximize: } 3x_1 + 5x_2 \]

Now, let's consider the constraints based on the available resources:
- Water constraint: A serving of crab soup requires 7 units of water, and a serving of lobster soup requires 10 units of water. There are 80 units of water available.
\[ 7x_1 + 10x_2 \leq 80 \]
- Crab meat constraint: A serving of crab soup requires 8 units of crab meat. There are 65 units of crab meat available.
\[ 8x_1 \leq 65 \]
- Lobster meat constraint: A serving of lobster soup requires 5 units of lobster meat. There are 55 units of lobster meat available.
\[ 5x_2 \leq 55 \]
- Non-negativity constraints: Since the number of servings cannot be negative, we have:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

The symbolic representation of the problem is thus:

```json
{
    'sym_variables': [('x1', 'number of servings of crab soup'), ('x2', 'number of servings of lobster soup')],
    'objective_function': '3*x1 + 5*x2',
    'constraints': [
        '7*x1 + 10*x2 <= 80',
        '8*x1 <= 65',
        '5*x2 <= 55',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

To solve this problem using Gurobi in Python, we'll use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="crab_soup_servings")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="lobster_soup_servings")

# Set the objective function
m.setObjective(3*x1 + 5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 10*x2 <= 80, "water_constraint")
m.addConstr(8*x1 <= 65, "crab_meat_constraint")
m.addConstr(5*x2 <= 55, "lobster_meat_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Crab soup servings: {x1.x}")
    print(f"Lobster soup servings: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```